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Статті в журналах з теми "Clinical medicine – Code numbers"
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Jackson, Michael, Marta Szczepaniak, Jasmine Wall, Mark Maskery, Catherine Mummery, Paul Morrish, Adrian Williams, Joanne Knight, and Hedley C. A. Emsley. "Numbers and types of neurological emergencies in England and the influence of socioeconomic deprivation: a retrospective analysis of hospital episode statistics data." BMJ Open 12, no. 11 (November 2022): e061843. http://dx.doi.org/10.1136/bmjopen-2022-061843.
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ObjectivesIn this first large-scale analysis of neurological emergency admissions in England, we determine the number and types of emergency admissions with neurological emergency diagnostic codes, how many are under the care of a neurologist or neurosurgeon and how such admissions vary by levels of deprivation.DesignRetrospective empirical research employing a derived list of neurological emergency diagnostic codesSettingThis study used the Hospital Episode Statistics data set for the financial year 2019/2020 based on 17 million in-year inpatient admissions in England including 6.5 million (100%) emergency admissions with any diagnosis codes.ResultsThere were 1.4 million (21.2%) emergency inpatient admissions with a mention of any neurological code, approx. 248 455 (3.8%) with mention of a specific neurological emergency code from the derived list, and 72 485 (1.1%) included such a code as the primary reason for admission. The highest number of in-year admissions for adults was for epilepsy (145 995), with epilepsy as the primary diagnostic code in 15 945 (10.9%). Acute nerve root/spinal cord syndrome (41 215), head injury (29 235) and subarachnoid haemorrhage (18 505) accounted for the next three highest number of admissions. 3230 (1.4%) in-year emergency hospital admissions with mention of a neurological emergency code were under the care of a neurologist or neurosurgeon, with only 1315 (0.9%) admissions with mention of an epilepsy code under a neurologist. There was significant variation for epilepsy and functional neurological disorders (FNDs) in particular by Index of Multiple Deprivation decile. The association between deprivation and epilepsy and FND was significant with p-values of 2.5e-6 and 1.5e-8, respectively.ConclusionsThis study has identified important findings in relation to the burden of neurological emergency admissions but further work is needed, with greater clinical engagement in diagnostic coding, to better understand the implications for workforce and changes to service delivery needing to be implemented.
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Tsumoto, Shusaku, Shoji Hirano, Tomohiro Kimura, and Haruko Iwata. "Mining Clinical Process from Hospital Information System: A Granular Computing Approach." Fundamenta Informaticae 182, no. 2 (September 30, 2021): 181–218. http://dx.doi.org/10.3233/fi-2021-2070.
Повний текст джерелаАнотація:
Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods.
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Denburg, Michelle R., Hanieh Razzaghi, L. Charles Bailey, Danielle E. Soranno, Ari H. Pollack, Vikas R. Dharnidharka, Mark M. Mitsnefes, et al. "Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research." Journal of the American Society of Nephrology 30, no. 12 (November 15, 2019): 2427–35. http://dx.doi.org/10.1681/asn.2019040365.
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BackgroundThe rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients.MethodsThe authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children’s hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798).ResultsThe final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months.ConclusionsThe authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.
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Petrini, Carlo. "Will medically-assisted suicide mean the rebirth of (clinical) ethics committees in Italy?" Medico-Legal Journal 88, no. 1_suppl (June 12, 2020): 26–30. http://dx.doi.org/10.1177/0025817220923650.
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The Italian Constitutional Court has held that, in certain specific circumstances, prosecution for assisted suicide, regulated by Article 580 of the Criminal Code, is not compatible with the Constitution. The circumstances in question relate to individuals who are being kept alive by life-sustaining treatments, who are fully capable of taking free, informed decisions and are suffering from irreversible conditions that are a source of intolerable physical or mental suffering. The Court has held that the Ethics Committees must assess a request for assisted suicide made by an individual meeting these conditions. The decision requires the identification of the Ethics Committee authorised to issue authorisation in such cases and a guarantee that these Ethics Committees are able to deal with this type of issue. The Court’s decision is an important opportunity to establish and promote clinical Ethics Committees, which are not nationally regulated in Italy and exist in very small numbers in only a few parts of the country.
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Liu, On Ying, Theodore Malmstrom, Patricia Burhanna, and Miriam B. Rodin. "The Evolution of an Inpatient Palliative Care Consultation Service in an Urban Teaching Hospital." American Journal of Hospice and Palliative Medicine® 34, no. 1 (July 11, 2016): 47–52. http://dx.doi.org/10.1177/1049909115610077.
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Background: Research on inpatient palliative medicine reports quality-of-life outcomes and selected “hard” outcomes including pain scores, survival, and readmissions. Objective: This case study reports the evolution of an inpatient palliative consultation (IPC) team to show how IPC induces culture change in a hospital that previously had no palliative care. Design: Retrospective chart review. Setting: A Catholic university-affiliated, inner-city hospital. Population: A total of 1700 consecutive adult inpatients from May 2009 to October 2013. Measures: Consultation records enumerated demographics, code status, powers of attorney, referring physician, reason for consultation, and discharge destination. Deidentified data were uploaded to a spreadsheet. Simple descriptive statistics were calculated. Results: Requests originated from internal medicine (24%), geriatrics (21%), neurology (including stroke and neurosurgery, 14.3%), medical intensive care unit (MICU, 12.2%), and hematology–oncology (10.3%). The MICU consults increased 17.6% over time. The numbers of consults nearly doubled after trainees began rounding with the service. Hospice discharges increased by 9.2%. Palliative management of in-hospital expirations increased 2- to 3-fold. The most common consultation requests were for pain and nonpain symptoms, establishing goals of care for patients experiencing clinical decline and convening family meetings in cases of divided judgment. Conclusion: We describe the evolution of palliative care in a safety-net hospital. Medicine services which are largely resident run adopted early. Specialty services that are attending driven adopted later. We believe house staff and nurses were the initial change agents. The number of consultations increased when house staff and students began rotating on the service suggesting unmet demand due to the limited supply of providers.
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Lam, Ching-Wan. "PrimeIndel: Four-prime-number genetic code for indel decryption and sequence read alignment." Clinica Chimica Acta 436 (September 2014): 1–4. http://dx.doi.org/10.1016/j.cca.2014.04.006.
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Wu, Ta-Peng, Cheng-Hung Tsai, Yu-Ting Su, Chu-Chiao Wang, Tzeng-Ji Chen, Ching-Mao Chang, and Fang-Pey Chen. "The Evaluation of Professional Divisions of Traditional Chinese Medicine in Taiwan through Patient Visit Records of 2012." International Journal of Environmental Research and Public Health 15, no. 9 (September 13, 2018): 1992. http://dx.doi.org/10.3390/ijerph15091992.
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For decades, professional divisions have been represented as the main structural divisions in Western medicine throughout the world. In Taiwan, medical policymakers are also interested in designing professional divisions of traditional Chinese medicine (TCM). Therefore, this study evaluated the current status and potentiality of professional divisions of TCM in Taiwan using data from the year 2012 obtained from the National Health Insurance Research Database; the database provides information regarding age and gender of TCM physicians (TCMPs); total visit counts; contracted medical institution codes; groupings of diseases classified under International Classification of Diseases, Ninth Revision, Clinical Modification codes; numbers of children and female patients seeking treatment; and claim disposition codes used by each TCMP. The results indicated that there were 5522 TCMPs in 2012, and 4876 (90.3%) TCMPs practiced in primary clinics. The proportions of pediatric visits to these TCMPs were mostly below 0.2, and acupuncture or traumatology-related visit proportions were below 0.5. Only a few of the studied Taiwan-based TCMPs practiced gynecology and pediatrics, but most of them performed “internal medicine”, or “acupuncture” or “traumatology” treatments. Thus, the number of TCM specialists practicing gynecology or pediatrics is insufficient, indicating that a policy that forms professional divisions of TCM practitioners in Taiwan should be reconsidered.
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Jeong, Yunjin, Haewook Jang, Junwon Kang, Juhong Nam, Kyoungseob Shin, Sunghoon Kwon, and Jungil Choi. "Color-Coded Droplets and Microscopic Image Analysis for Multiplexed Antibiotic Susceptibility Testing." Biosensors 11, no. 8 (August 19, 2021): 283. http://dx.doi.org/10.3390/bios11080283.
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Since the discovery of antibiotics, the emergence of antibiotic resistance has become a global issue that is threatening society. In the era of antibiotic resistance, finding the proper antibiotics through antibiotic susceptibility testing (AST) is crucial in clinical settings. However, the current clinical process of AST based on the broth microdilution test has limitations on scalability to expand the number of antibiotics that are tested with various concentrations. Here, we used color-coded droplets to expand the multiplexing of AST regarding the kind and concentration of antibiotics. Color type and density differentiate the kind of antibiotics and concentration, respectively. Microscopic images of a large view field contain numbers of droplets with different testing conditions. Image processing analysis detects each droplet, decodes color codes, and measures the bacterial growth in the droplet. Testing E. coli ATCC 25922 with ampicillin, gentamicin, and tetracycline shows that the system can provide a robust and scalable platform for multiplexed AST. Furthermore, the system can be applied to various drug testing systems, which require several different testing conditions.
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Schmidt, Lena, Ailbhe N. Finnerty Mutlu, Rebecca Elmore, Babatunde K. Olorisade, James Thomas, and Julian P. T. Higgins. "Data extraction methods for systematic review (semi)automation: Update of a living systematic review." F1000Research 10 (October 9, 2023): 401. http://dx.doi.org/10.12688/f1000research.51117.2.
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Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually.
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Ceylan, Ahmet Cevdet, Haktan Bağış Erdem, İbrahim Şahin, and Meenal Agarwal. "SMN1 gene copy number analysis for spinal muscular atrophy (SMA) in a Turkish cohort by CODE-SEQ technology, an integrated solution for detection of SMN1 and SMN2 copy numbers and the “2+0” genotype." Neurological Sciences 41, no. 9 (April 6, 2020): 2575–84. http://dx.doi.org/10.1007/s10072-020-04365-x.
Повний текст джерелаДисертації з теми "Clinical medicine – Code numbers"
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Kaur, Rajvir. "A comparative analysis of selected set of natural language processing (NLP) and machine learning (ML) algorithms for clinical coding using clinical classification standards." Thesis, 2018. http://hdl.handle.net/1959.7/uws:49614.
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In Australia, hospital discharge summaries created at the end of an episode of care contain patient information such as demographic data, medical history, various diagnosis, interventions carried out, medications and drug therapies provided to the patient. These discharge summaries not only serve as a record of the episode of care, but later converted into a set of clinical codes for statistical analysis purposes. The process of clinical coding refers to assigning alphanumeric codes to discharge summaries. In Australia, clinical coding is done using International Classification of Diseases, version 10, Australian Modification (ICD-10-AM) and Australian Classification of Health Interventions (ACHI) as per the Australian Coding Standards (ACS), in an acute and subacute care setting, in both public and private hospitals. Clinical coding and subsequent analysis facilitate funding, insurance claims processing and research. The task of assigning codes to an episode of care is a manual process. This posed challenges in terms of ever-increasing set of codes in ICD-10-AM and ACHI, changing coding standards in ACS, complexity of care episodes, and large training and recruitment costs associated with clinical coders. In addition, the manual clinical coding process is time consuming and prone to errors, leading to financial losses. The use of Natural Language Processing (NLP) and Machine Learning (ML) techniques is considered as a solution to the above problem. In this thesis, four different approaches namely, pattern matching, rule based, machine learning and hybrid technique are compared to identify most efficient algorithm suitable for clinical coding. The ICD-10-AM and ACHI consists of 22 chapters based on human body organs, where each chapter describe diseases and interventions of a body system. The aforementioned, NLP and ML comparison is carried out only two chapters namely, diseases of the respiratory system and diseases of the digestive system. Initially, the dataset contained 190 clinical records of two chapters and named as Data190. Due to the limited number of clinical records, another 45 records were added to the existing dataset and this resultant dataset was named as Data235. The clinical records were cleaned up in the pre-processing stage to extract useful information which includes principal diagnosis, additional diagnosis, diabetes condition, principal procedure, additional procedure and anaesthesia details. In data pre-processing, various NLP techniques such as tokenisation, stop word removal, spelling error detection and correction, negation detection and abbreviation expansion were applied. In pattern matching approach, the textstring were matched charcter by character against the ICD-10-AMand ACHI coding guide using regular expression. If the match was found, codes were assigned. Whereas, in rule-based, 409 rules were defined to avoid coding of wrong patterns. In machine learning, once the unwanted information was removed from the clinical records, text was represented in vector form for feature extraction using Bag of words (BoW) representation (Manning, Raghavan, & Schütze, 2008, p. 117) and Term Frequency-Inverse Document Frequency (TF-IDF) vectoriser (Manning et al., 2008, p. 118). After feature extraction, classification is done using seven classifiers namely Support Vector Machine (SVM) (Cortes & Vapnik, 1995), Naïve Bayes (Manning et al., 2008, p. 258), Decision Tree (Kumar, Assistant, & Sahni, 2011), Random Forest (Breiman, 2001), AdaBoost (Freund & Schapire, 1999), Multi Layer Perceptron (MLP) (Naraei, Abhari, & Sadeghian, 2016) and k-Nearest Neighbour (kNN) (Manning et al., 2008, p. 297). A set of standard metrics: Precision(P), Recall (R), F-score (F-score), Accuracy, Hamming Loss(HL) and Jaccard Similarity (JS) (Dalianis, 2018), (Aldrees & Chikh, 2016) is used to do the measure the efficiency of the said NLP and ML algorithms using the above mentioned two datasets. For both the datasets (Data190 and Data235), the machine learning approach and the hybrid approach gave good performances in comparison to pattern matching and rule-based approach. Among all the classifiers, AdaBoost outperformed followed by Decision Tree and other classifiers. In the machine learning approach, Decision Tree technique performed better than all the other classifiers using 4-gram feature set by achieving 0.87 F-score, 0.7453 JS and 0.0877 HL. Similarly, in Data235, AdaBoost outperforms by achieving 0.91 F-score, 0.8294 JS and 0.0945 HL.
Книги з теми "Clinical medicine – Code numbers"
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Instructor's manual to accompany Medical coding specialist's exam review--physician. Clifron Park, NY: Delmar/Thomson Learning, 2005.
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Association, American Medical, ed. CPT reference of clinical examples: Official scenarios for correct coding. [Chicago]: American Medical Association, 2007.
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(Firm), OptumInsight, ed. ICD-9-CM for hospitals and payers, volumes 1, 2, 3: 2014 expert : international classification of diseases, 9th revision ; clinical modificaiton, sixth edition. [Eden Prairie, Minn.]: OptumInsight, 2013.
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Green, Michelle A. 3-2-1 code it! Clifton Park, NY: Thomson Delmar Learning, 2007.
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Nobrega, Fred T. Feasibility of using CPT-4 codes for DRG assignment of surgical patients. Rochester, Minn: Mayo Foundation, Dept. of Internal Medicine, 1985.
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ICD-10-CM complete draft code set: Draft 2014 clinical modification. 2nd ed. Eden Prairie, MN: OptumInsight, 2014.
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Healthcare code sets, clinical terminologies, and classification systems. 2nd ed. Chicago, Ill: American Health Information Management Association, 2010.
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Kathy, Giannangelo, ed. Healthcare code sets, clinical terminologies, and classification systems. Chicago, Ill: American Health Information Management Association, 2006.
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World Health Organization (WHO). ICD-9-CM, 2013. Los Angeles, Calif: Practice Management Information Corp., 2012.
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Jr, Bradley Joel, and American Academy of Pediatrics. RBRVS Project Advisory Committee., eds. Coding for pediatrics: A manual for pediatric documentation and reimbursement. 5th ed. Elk Grove Village, IL: American Academy of Pediatrics, 1999.
Знайти повний текст джерелаЧастини книг з теми "Clinical medicine – Code numbers"
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Cassidy, C. M. "Beyond Numbers: Qualitative Research Methods for Oriental Medicine." In Clinical Acupuncture, 151–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56732-2_9.
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Littmann, Jasper, A. M. Viens, and Diego S. Silva. "The Super-Wicked Problem of Antimicrobial Resistance." In Ethics and Drug Resistance: Collective Responsibility for Global Public Health, 421–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27874-8_26.
Повний текст джерелаАнотація:
Abstract Antimicrobial resistance (AMR) – the progressive process by which microbes, such as bacteria, through evolutionary, environmental and social factors develop the ability to become resistant to drugs that were once effective at treating them – is a threat from which no one can escape. It is one of the largest threats to clinical and global health in the twenty-first century – inflicting monumental health, economic and social consequences. All persons locally and globally, and even all future persons yet to come into existence, all suffer the shared, interdependent vulnerability to this threat that will have a substantial impact on all aspects of our lives. For example, while reliable data are hard to find, the European Centre for Disease Prevention and Control (ECDC) has conservatively estimated that, in Europe alone, AMR causes additional annual cost to health care systems of at least €1.5 billion, and is responsible for around 25,000 deaths per year. Furthermore, AMR significantly increases the cost of treating bacterial infections with an increase in length of hospital stays and average number of re-consultations, as well as the resultant lost productivity from increased morbidity. With a combined cost of up to $100 trillion to the global economy – pushing a further 28 million people into extreme poverty – this is one of the most pressing challenges facing the world. Most troublingly, if we do not succeed in diminishing the progression of AMR, there is the very real potential for it to threaten common procedures and treatments of modern medicine, including the safety and efficacy of surgical procedures and immunosuppressing chemotherapy. Some experts are warning that we may soon be ushering in a post-antibiotic area.
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Furmedge, Dan. "Clinical Chemistry." In Oxford Assess and Progress: Clinical Medicine. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198812968.003.0019.
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With so many tests available and increasingly fast laboratory processors, there is a growing temptation to request large numbers of blood tests on each and every patient. What we should remember is that they should be used as an adjunct to the history and clinical examination. Test results should reinforce the likely diagnosis and rule out our differentials, rather than be used to try and make the diagnosis per se. What makes things easier is to know how serum biochemistry and homeostasis are regulated and then to consider a number of questions: ● Which hormones are involved in the control of this electrolyte? ● What happens when these are increased or decreased? ● Does the patient have any renal or hepatic impairment? ● Are they taking any drugs that might be affecting serum electrolyte levels? ● Have they taken an overdose? ● Is the patient dehydrated/ hypovolaemic/ hypoxic? It can seem daunting at first when results come back unexpectedly out of range. They must be considered in combination with the patient’s clinical status; if the numbers just do not fit, then repeat the test— they may not be right. However, there are a few ‘unmissable’ electrolyte derangements that need to be dealt with immediately. Once detected, they should trigger the thoughts shown in Table 12.1. Interpreting serum values is important, but to prevent iatrogenic derangement, careful use and prescription of intravenous fluids are also needed. Does a patient who looks hypovolaemic, has a low blood pressure, and is tachycardic need crystalloids or blood? Colloids, such as ‘Gelofusine®’ or ‘Volplex®’, are now largely out of date, with evidence not supporting their use. Are they frail or do they have cardiac failure and therefore require cautious replacement? Do they have liver failure? Is their fluid balance so critical that close fluid monitoring in a level 2 critical care setting required? Gone are the days when central venous lines are used on the wards for fluid balance. This chapter will help consideration of the whole picture before putting pen to paper and (potentially) wrongly prescribing 4 L of fluids a day for a frail older man with left ventricular failure.
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Davies, Mark, and Julian Sampson. "Genomics and Cancer: Mechanisms and Applications." In Genomics and Clinical Medicine, 343–53. Oxford University PressNew York, NY, 2008. http://dx.doi.org/10.1093/oso/9780195188134.003.0023.
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Abstract Cancer is diagnosed in one in three of those living in the ‘‘developed world’’ and causes the death of one in four. Longevity is a major factor in the health impact of cancer since the incidence of most common cancers shows a log-linear relationship with age (Hanson et al., 1980). Environmental and social factors including smoking, diet and the trend to a delay, and reduction in numbers of pregnancies are also of striking importance in determining the pattern of cancers seen in contemporary society. Nonetheless, cancer is fundamentally a genetic disease. Its development is determined by the accumulation of mutations in genes that normally coordinate the birth and death of cells to ensure that they are produced only as and when they are required and their regulate behavior such that they remain under control at the level of tissue, organ, and organism. Over the last three decades, our understanding of cancer has been transformed by the identification of many genes that are mutated, or whose expression is altered in cancers, and by the characterization of their functions that are lost or changed as a cancer develops. Key mechanisms of tumorigenesis have been established, and many of the signaling and regulatory pathways that are hijacked by cancers have been elucidated, often through genetic approaches. Many advances that have impacted on knowledge of cancer have come initially from genetic studies of simple organisms, notably bacteria and yeast where comprehensive experimental strategies could be adopted.
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George, Naomi, and Corita Grudzen. "Palliative care in the emergency department." In Oxford Textbook of Palliative Medicine, edited by Nathan I. Cherny, Marie T. Fallon, Stein Kaasa, Russell K. Portenoy, and David C. Currow, 133–42. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780198821328.003.0014.
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The emergency department provides immediate access to medical care for patients and families in crisis. As the proportion of elderly in our population continues to increase, complications related to advanced illness will drive increasing numbers of patients with palliative care needs to the emergency department for care. In addition to immediately life-threatening situations, severe physical symptoms, psychological distress, caregiver burdens, and unrecognized psychological or spiritual crises can overwhelm patients or their caregivers, who come to the emergency department hoping for relief from their suffering. When treating patients with serious illness, emergency clinicians must rapidly assess the situation at hand, provide time-sensitive clinical interventions, and engage in effective and efficient communication pertaining to often complex and charged topics. This chapter addresses several of the essential clinical skills for effective primary palliative care in the emergency department, including care of the imminently dying patient, as well as care of patients who are seriously ill who are not yet facing a terminal event. Essential skills include rapid identification of patients with palliative care needs, efficiently establishing treatment goals and a treatment pathway, titration of medications for symptom emergencies, skilful and empathic death disclosure, and a structured approach to involving family presence during resuscitation efforts.
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Furmedge, Dan. "Geriatric medicine." In Oxford Assess and Progress: Clinical Medicine, 475–90. 4th ed. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/med/9780192868411.003.0016.
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Abstract Geriatric medicine is the largest ‘medical’ specialty in the United Kingdom, with the number of geriatricians expanding due to and ageing population. Managing frailty and complex medical comorbidity is the core skill of a geriatrician. In this chapter, questions give a taste of some of the common clinical areas in geriatric medicine and will demonstrate how geriatric medicine crosses almost every specialty. This chapter contains 18 questions that encompass many of the important areas of this subject, with detailed explanations. Unique to this series, questions are rated by difficulty and are cross-referenced to the eleventh edition of Oxford Handbook of Clinical Medicine to track revision progress and revise effectively.
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Burd, Mary, and Jan Wiener. "Therapists and counsellors in primary care." In Mental Health in Primary Care—a new approach, 147–64. Oxford University PressOxford, 2002. http://dx.doi.org/10.1093/oso/9780198508946.003.0011.
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Abstract Most counsellors and psychotherapists enjoy working in primary care. Because such therapists have not been recognized by any of the NHS pay scales, their independent contractor status has enabled GPs to employ them directly and so tempting opportunities for work have been more available, especially for people who do not have a core professional background in psychology, medicine, or social work. Such posts, when advertised, attract large numbers of applicants, all apparently rushing to the front line to work with patients in distress at the site where they most commonly first present themselves. There are now about 3000 part-time counsellors employed in practices across the country, amounting to 1300 full-time posts (Mellor-Clarke 2000). At the same time, there is also a tradition of other NHS employed staff, such as clinical psychologists and CPNs working in primary care.
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Furmedge, Dan. "Geriatric Medicine." In Oxford Assess and Progress: Clinical Medicine. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198812968.003.0023.
Повний текст джерелаАнотація:
Geriatric medicine is the largest ‘medical’ specialty in the United Kingdom, with the number of geriatricians expanding at a huge rate with significant demand. Pragmatic specialists in frailty and complex co- morbidity, the work of geriatricians reaches across geriatric medicine wards, the acute medical unit, emergency departments and acute frailty units, surgical wards, and tertiary medical wards and in the community from inner city London to rural Scotland. They can be found in residential and nursing care homes, rehabilitation teams, and hospital at home teams. Frailty, falls, delirium, dementia, continence, immobility, rehabilitation, polypharmacy, nutrition, end- of- life care, advanced care planning, community medicine, and legal and ethical medicine are all core features of a geriatrician’s day. In this chapter, the questions give a taste of some of these concepts and will also demonstrate how geriatric medicine crosses almost every specialty.
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Landsberg, Judd W. "Code status." In Clinical Practice Manual for Pulmonary and Critical Care Medicine, 329–31. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-323-39952-4.00029-9.
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Ghaemi, S. Nassir. "Evidence-Based Medicine." In Clinical Psychopharmacology, edited by S. Nassir Ghaemi, 59–63. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199995486.003.0005.
Повний текст джерелаАнотація:
Evidence-based medicine (EBM) is the view that there are levels of evidence, with higher ones being more valid than lower ones. The higher levels of evidence have less confounding bias, with randomization being the best method to minimize such bias. Lower levels of evidence have more confounding bias, with clinical experience being the worst setting for such bias. However, evidence-based medicine is misinterpreted if it is oversimplified. The EBM literature has various definitions of specific levels of evidence. Standard EBM text uses letters (A through D). One could also use numbers (I through V). The basic idea is that randomized studies are higher levels of evidence than non-randomized studies, and that the lowest level of evidence consists of case reports, expert opinion, or the consensus of the opinion of clinicians or investigators. The pros and cons of the EBM approach are scrutinized in this chapter.
Тези доповідей конференцій з теми "Clinical medicine – Code numbers"
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Stoicescu, Ramona, Razvan-Alexandru Stoicescu, Codrin Gheorghe, Adina Honcea, and Iulian Bratu. "CONSIDERATIONS ON SARS-COV-2 DIAGNOSIS IN THE LABORATORY OF UNIVERSITY EMERGENCY CLINICAL HOSPITAL OF CONSTANTA." In GEOLINKS Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/geolinks2021/b1/v3/07.
Повний текст джерелаАнотація:
Coronaviruses are members of the Coronaviridae family. They are enveloped, non-segmented, positive-sense, single-stranded RNA viruses. Their genome size is about 30 kilobases (kb) which consist, at the 5’ end, of non-structural open reading frames (ORFs: ORF1a, ORF 1b) which code for 16 non structural proteins, and at the 3’ end the genes which code for four structural proteins including membrane (M), envelope (E), spike (S), and nucleocapsid (N) proteins. Due to the rapid spread of COVID-19, a reliable detection method is needed for patient diagnosis especially in the early stages of the disease. WHO has recommended nucleic acid amplification tests such as real-time reverse transcription-polymerase chain reaction (RT-PCR). The assay detects three SARS-CoV-2 RNA targets: the envelope (E) gene, the nucleocapsid (N) gene and a region of the open reading frame (ORF1) of the RNA-dependent RNA polymerase (RdRp) gene from SARS-CoV-2 virus isolate Wuhan-Hu-1. Our study was made in the first 3 months of the year 2021 using the real-time RT PCR results obtained in the Cellular Biology ward of the University Emergency Clinical Hospital. In our lab we are testing the inpatients from the hospital wards (Neurology, Pediatrics, Surgery, Internal medicine, ICU, Cardiology, etc.); we are also testing the outpatients from Dialysis and Oncology, 2 days prior to their therapy; we also test the health care personnel. The number of tests we performed was: in January 1456, with 399 positive results (27.4%), 33 deaths; in February 1273 tests, 221 positive (17.36%), 16 deaths; in March 1471 tests, 373 positive (25.36%), 37 deceased.
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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.
Повний текст джерелаАнотація:
Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.
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Joseph, Verlin, Deepthi Varma, Xinguang Chen, Roger Fillingim, Diana Wilkie, and Robert Cook. "Marijuana Use Among Persons Living with Chronic Pain: A Qualitative Study." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.27.
Повний текст джерелаАнотація:
Chronic pain is presently the most expensive disease condition in the United States; with roughly 1 in 5 Americans currently reporting chronic pain. Traditionally, clinicians and patients have utilized prescription medications to manage chronic pain, however, attitudes towards standardized prescription treatment have waned due to a number of factors including the recent opioid epidemic. Consequently, attitudes towards marijuana use as an alternative pain therapy have continued to grow with 36 states approving use for medical purposes. Clinical trials have reported lower pain severity scores among participants receiving marijuana for pain compared to participants in the control arms. However, little is known regarding the psychological/physiological mechanisms by which marijuana provides relief for chronic pain. Research is also needed to identify how current marijuana users developed optimal patterns (i.e. dose, frequency, and quantity) of use for chronic pain. We sought to address these scientific gaps by conducting semi-structured interviews among community members. Participants (N=13) were eligible for participation if they reported having any pain during the previous 3 months and if they reported past 30-day marijuana use. The interviews lasted approximately 60 minutes following a semi-structured interview guide over Zoom telecommunications. Each interview was subsequently transcribed and then analyzed using Atlas T.I. A summary of each code was developed and key themes regarding marijuana use and pain were summarized. Multiple themes emerged including mechanism of pain relief (physical, mental, or both), patterns of marijuana use for pain relief, other benefits of marijuana use, comparisons to prescription drug use, descriptions of pain, and side effects of marijuana use. Our analysis identified several themes that add to the growing literature concerning marijuana use for chronic pain. These qualitative data indicate current marijuana users’ favorable opinions towards using marijuana as an alternative pain therapy. As legislation legalizing marijuana use for medicinal purposes continues to expand, qualitative studies are needed to examine the extent to which marijuana use is effective in managing pain.
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Bockman, Matthew D., Igor I. Katkov, Stephen B. Jones, Vsevolod Katkov, and Ilya Yakhnenko. "ComfortFreezer™: A Benchtop LN2–Free Programmable Freezer for Cryopreservation of Adherent Cells in Multi-Well Plates for Cell-Based High Content Screening." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-85469.
Повний текст джерелаАнотація:
Human pluripotent stem cells (hPSCs) and their progeny such as hPSC-derived cardiomyocytes and neural cells hold great potential as a source for cell therapy and regenerative medicine, as well can be effectively used for high high content screening (HCS) of drug candidates and for toxicity tests. Cryopreservation (CP), storage, and shipment of the cells are key elements for eventual clinical, pharmaceutical and environmental applications, which will require large numbers of quality controlled and ready for use cells. Traditionally, the cells are frozen in suspensions of either fully dissociated cells) or loosely associated clusters such as clumps of hPSCs, clusters of beaters”of cardiomyocytes, (“or neurospheres of neural precursors. Beside logistical inconvenience for some applications such as HCS, additional manipulation with the cells (detachment, dissociation and centrifugation) can introduce substantial stress to the cells prior to freezing and after thawing, which per se may tremendously decrease the cell cryosurvival and functionality. Here, we are presenting ComfortFreezer™, a novel bench-top device specifically designed for cryopreservation in multi-well plates for cell-based high content screening (HCS), which combines a liquid-nitrogen (LN2) free programmable freezer This cryogenic equipemnt can bring serious advantage for HCS in drug screening, environmental toxicity evaluation, and other variety of HCS-based applications.
Звіти організацій з теми "Clinical medicine – Code numbers"
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MacFarlane, Andrew. 2021 medical student essay prize winner - A case of grief. Society for Academic Primary Care, July 2021. http://dx.doi.org/10.37361/medstudessay.2021.1.1.
Повний текст джерелаАнотація:
As a student undertaking a Longitudinal Integrated Clerkship (LIC)1 based in a GP practice in a rural community in the North of Scotland, I have been lucky to be given responsibility and my own clinic lists. Every day I conduct consultations that change my practice: the challenge of clinically applying the theory I have studied, controlling a consultation and efficiently exploring a patient's problems, empathising with and empowering them to play a part in their own care2 – and most difficult I feel – dealing with the vast amount of uncertainty that medicine, and particularly primary care, presents to both clinician and patient. I initially consulted with a lady in her 60s who attended with her husband, complaining of severe lower back pain who was very difficult to assess due to her pain level. Her husband was understandably concerned about the degree of pain she was in. After assessment and discussion with one of the GPs, we agreed some pain relief and a physio assessment in the next few days would be a practical plan. The patient had one red flag, some leg weakness and numbness, which was her ‘normal’ on account of her multiple sclerosis. At the physio assessment a few days later, the physio felt things were worse and some urgent bloods were ordered, unfortunately finding raised cancer and inflammatory markers. A CT scan of the lung found widespread cancer, a later CT of the head after some developing some acute confusion found brain metastases, and a week and a half after presenting to me, the patient sadly died in hospital. While that was all impactful enough on me, it was the follow-up appointment with the husband who attended on the last triage slot of the evening two weeks later that I found completely altered my understanding of grief and the mourning of a loved one. The husband had asked to speak to a Andrew MacFarlane Year 3 ScotGEM Medical Student 2 doctor just to talk about what had happened to his wife. The GP decided that it would be better if he came into the practice - strictly he probably should have been consulted with over the phone due to coronavirus restrictions - but he was asked what he would prefer and he opted to come in. I sat in on the consultation, I had been helping with any examinations the triage doctor needed and I recognised that this was the husband of the lady I had seen a few weeks earlier. He came in and sat down, head lowered, hands fiddling with the zip on his jacket, trying to find what to say. The GP sat, turned so that they were opposite each other with no desk between them - I was seated off to the side, an onlooker, but acknowledged by the patient with a kind nod when he entered the room. The GP asked gently, “How are you doing?” and roughly 30 seconds passed (a long time in a conversation) before the patient spoke. “I just really miss her…” he whispered with great effort, “I don’t understand how this all happened.” Over the next 45 minutes, he spoke about his wife, how much pain she had been in, the rapid deterioration he witnessed, the cancer being found, and cruelly how she had passed away after he had gone home to get some rest after being by her bedside all day in the hospital. He talked about how they had met, how much he missed her, how empty the house felt without her, and asking himself and us how he was meant to move forward with his life. He had a lot of questions for us, and for himself. Had we missed anything – had he missed anything? The GP really just listened for almost the whole consultation, speaking to him gently, reassuring him that this wasn’t his or anyone’s fault. She stated that this was an awful time for him and that what he was feeling was entirely normal and something we will all universally go through. She emphasised that while it wasn’t helpful at the moment, that things would get better over time.3 He was really glad I was there – having shared a consultation with his wife and I – he thanked me emphatically even though I felt like I hadn’t really helped at all. After some tears, frequent moments of silence and a lot of questions, he left having gotten a lot off his chest. “You just have to listen to people, be there for them as they go through things, and answer their questions as best you can” urged my GP as we discussed the case when the patient left. Almost all family caregivers contact their GP with regards to grief and this consultation really made me realise how important an aspect of my practice it will be in the future.4 It has also made me reflect on the emphasis on undergraduate teaching around ‘breaking bad news’ to patients, but nothing taught about when patients are in the process of grieving further down the line.5 The skill Andrew MacFarlane Year 3 ScotGEM Medical Student 3 required to manage a grieving patient is not one limited to general practice. Patients may grieve the loss of function from acute trauma through to chronic illness in all specialties of medicine - in addition to ‘traditional’ grief from loss of family or friends.6 There wasn’t anything ‘medical’ in the consultation, but I came away from it with a real sense of purpose as to why this career is such a privilege. We look after patients so they can spend as much quality time as they are given with their loved ones, and their loved ones are the ones we care for after they are gone. We as doctors are the constant, and we have to meet patients with compassion at their most difficult times – because it is as much a part of the job as the knowledge and the science – and it is the part of us that patients will remember long after they leave our clinic room. Word Count: 993 words References 1. ScotGEM MBChB - Subjects - University of St Andrews [Internet]. [cited 2021 Mar 27]. Available from: https://www.st-andrews.ac.uk/subjects/medicine/scotgem-mbchb/ 2. Shared decision making in realistic medicine: what works - gov.scot [Internet]. [cited 2021 Mar 27]. Available from: https://www.gov.scot/publications/works-support-promote-shared-decisionmaking-synthesis-recent-evidence/pages/1/ 3. Ghesquiere AR, Patel SR, Kaplan DB, Bruce ML. Primary care providers’ bereavement care practices: Recommendations for research directions. Int J Geriatr Psychiatry. 2014 Dec;29(12):1221–9. 4. Nielsen MK, Christensen K, Neergaard MA, Bidstrup PE, Guldin M-B. Grief symptoms and primary care use: a prospective study of family caregivers. BJGP Open [Internet]. 2020 Aug 1 [cited 2021 Mar 27];4(3). Available from: https://bjgpopen.org/content/4/3/bjgpopen20X101063 5. O’Connor M, Breen LJ. General Practitioners’ experiences of bereavement care and their educational support needs: a qualitative study. BMC Medical Education. 2014 Mar 27;14(1):59. 6. Sikstrom L, Saikaly R, Ferguson G, Mosher PJ, Bonato S, Soklaridis S. Being there: A scoping review of grief support training in medical education. PLOS ONE. 2019 Nov 27;14(11):e0224325.
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Cohen, Deborah J., Annette M. Totten, Robert L. Phillips, Jr., Yalda Jabbarpour, Anuradha Jetty, Jennifer DeVoe, Miranda Pappas, Jordan Byers, and Erica Hart. Measuring Primary Healthcare Spending. Agency for Healthcare Research and Quality (AHRQ), May 2024. http://dx.doi.org/10.23970/ahrqepctb44.
Повний текст джерелаАнотація:
Background. Policy leaders and researchers have identified a range of primary care spending conceptualizations, developed frameworks and methods for measuring primary care spending, and documented the pros and cons of different approaches. However, these efforts have not been comprehensive, particularly as the number of estimates has grown. We continue this work by identifying the definitions, data sources, and approaches used to estimate primary care spending in the United States. Our objective was to identify where there is and is not consensus across methods, and how initial steps toward a standardized approach to estimating primary care spending might be achieved. We approached this comparison from a societal economic perspective. Methods. Searches were conducted in Ovid MEDLINE® and Cochrane CENTRAL databases (inception to May 2, 2023), and were supplemented by manual reviews of reference lists, Scopus searches of key articles, gray literature searches of State and organization websites, and responses to a Federal Register Notice, as well as recommendations from Key Informants. Websites of States and organizations that produced reports were reviewed in November 2023 to identify updates. Publicly available estimates and reports of methods were supplemented by discussions with experts who have supported States’ estimates. Findings. We identified 67 primary care spending estimates for 2010 to 2021: 42 of these were produced by 11 State Governments for their State, 2 were published by the Veterans Health Administration, and 23 were published by researchers or other organizations, which include foundations and policy organizations. Forty-four estimates reported on primary care spending for a single State, one estimate reported spending for the New England States, and 22 reported national spending. To date, 13 State Governments have developed and/or are implementing measurements of primary care spending. When State Governments measure primary care spending, they produce regular, often yearly, estimates. States have produced one to eight estimates, demonstrating some States have more experience with this task than others. Primary care spending estimates in our sample ranged from 3.1 to 10.3 percent. These estimates started with definitions of primary care, which are often labeled narrow or broad. Estimates may use these same labels to mean different things. Narrow definitions of primary care usually include fewer providers, locations, or service types, while broad definitions include more. State, regional, or national estimates are either reported as two estimates, one using a narrow and one using a broad definition of primary care, or as a single estimate labeled neither narrow nor broad. Variations in what providers, services, and locations are included in definitions of primary care are significant and likely contribute to variation in primary care spending estimates. However, it is difficult to distinguish differences in definitions and measurement from differences in actual primary care spending. Conclusions. While there are some core similarities in how primary care spending is measured across State, regional, and national estimates, there are more differences. While there may be rationale behind some of these variations, this variation limits comparisons and what could be understood about the impact of policies. Furthermore, lack of clear, detailed reporting of methods can obscure precisely how and why estimates differ. Research is needed that quantifies the impact different decisions and measurement methods have on spending estimates. To assure the validity and reliability of estimates of primary care spending, and facilitate comparisons and links to health outcomes, Federal, State, and policy leaders need to: (1) collaborate to create a primary care clinician database that can function as a public utility for States to allow for more precise identification of primary care clinics and clinicians, and reduce reliance on Current Procedural Terminology/Healthcare Common Procedure Coding System codes; (2) develop a template for transparent reporting of methods used to estimate primary care spending; (3) foster collaboration among Federal agencies and State leaders to develop a consensus definition of primary care and process for estimating primary care spending, with consideration of methods that are easy to understand and transparent; and (4) support the development and ongoing maintenance of State All-Payer Claims Databases, expand to include nonclaims payments, and supply Medicare and Medicaid estimates for every State.