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Journal articles on the topic 'Accuracy diagnosis'

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Author: Grafiati

Published: 25 May 2024

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1

Agarwal, Saumya, and Mamta Gupta. "Diagnostic Accuracy of Cytological Sampling Techniques by Bronchoscopy in the Diagnosis of Lung Cancer." Annals of Pathology and Laboratory Medicine 5, no. 5 (May 29, 2018): A354–361. http://dx.doi.org/10.21276/apalm.1720.

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2

Mandia, Sayati. "Accuracy of Diagnosis Coding Based On ICD-10." Asian Pacific Journal of Health Sciences 7, no. 1 (March 30, 2020): 43–47. http://dx.doi.org/10.21276/apjhs.2020.7.1.8.

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3

Koss, Leopold G. "Cytology. Accuracy of diagnosis." Cancer 64, S1 (July 1, 1989): 249–52. http://dx.doi.org/10.1002/1097-0142(19890701)64:1+<249::aid-cncr2820641313>3.0.co;2-2.

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4

Pankaj Javale, Deepali, and Sharmishta Desai. "Healthcare Critical Diagnosis Accuracy." International journal of electrical and computer engineering systems 14, no. 8 (October 24, 2023): 927–34. http://dx.doi.org/10.32985/ijeces.14.8.10.

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Since at least a decade, Machine Learning has attracted the interest of researchers. Among the topics of discussion is the application of Machine Learning (ML) and Deep Learning (DL) to the healthcare industry. Several implementations are performed on the medical dataset to verify its precision. The four main players, True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), play a crucial role in determining the classifier's performance. Various metrics are provided based on the main players. Selecting the appropriate performance metric is a crucial step. In addition to TP and TN, FN should be given greater weight when a healthcare dataset is evaluated for disease diagnosis or detection. Thus, a suitable performance metric must be considered. In this paper, a novel machine learning metric referred to as Healthcare-Critical-Diagnostic-Accuracy (HCDA) is proposed and compared to the well-known metrics accuracy and ROC_AUC score. The machine learning classifiers Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) are implemented on four distinct datasets. The obtained results indicate that the proposed HCDA metric is more sensitive to FN counts. The results show, that even if there is rise in %FN for dataset 1 to 10.31 % then too accuracy is 83% ad HCDA shows correlated drop to 72.70 %. Similarly, in dataset 2 if %FN rises to 14.80 for LR classifier, accuracy is 78.2 % and HCDA is 63.45 %. Similar kind of results are obtained for dataset 3 and 4 too. More FN counts result in a lower HCDA score, and vice versa. In common exiting metrics such as Accuracy and ROC_AUC score, even as the FN count increases, the score increases, which is misleading. As a result, it can be concluded that the proposed HCDA is a more robust and accurate metric for Critical Healthcare Analysis, as FN conditions for disease diagnosis and detection are taken into account more than TP and TN.

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Hu, Yifeng, and Jessica Haake. "Search Your Way to an Accurate Diagnosis: Predictors of Internet-Based Diagnosis Accuracy." Atlantic Journal of Communication 18, no. 2 (April 23, 2010): 79–88. http://dx.doi.org/10.1080/15456870903554916.

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6

Rawlinson, William D., Jenna M. Iwasenko, Peter W. Robertson, and Peter C. Taylor. "The Accuracy of Influenza Diagnosis." Infection Control & Hospital Epidemiology 32, no. 1 (January 2011): 98. http://dx.doi.org/10.1086/657672.

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7

MacKenzie-Wood, Andrew R., Gerry W. Milton, and John W. Launey. "Melanoma: Accuracy of clinical diagnosis." Australasian Journal of Dermatology 39, no. 1 (February 1998): 31–33. http://dx.doi.org/10.1111/j.1440-0960.1998.tb01238.x.

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8

Kumaran, N., Amir Azmy, Robert Carachi, Peter A. M. Raine, Jeanette H. Macfarlane, and Alan G. Howatson. "Pilomatrixoma—accuracy of clinical diagnosis." Journal of Pediatric Surgery 41, no. 10 (October 2006): 1755–58. http://dx.doi.org/10.1016/j.jpedsurg.2006.05.005.

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9

Gelfand, David W., David J. Ott, H. Alexander Munitz, and Yu Men Chen. "CandidaEsophagitis: Accuracy of Radiographic Diagnosis." Radiology 156, no. 3 (September 1985): 844. http://dx.doi.org/10.1148/radiology.156.3.844-a.

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10

Amrikachi, Mojghan, Ibrahim Ramzy, Sheldon Rubenfeld, and Thomas M. Wheeler. "Accuracy of Fine-Needle Aspiration of Thyroid." Archives of Pathology & Laboratory Medicine 125, no. 4 (April 1, 2001): 484–88. http://dx.doi.org/10.5858/2001-125-0484-aofnao.

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Abstract Context.—Fine-needle aspiration has become an accepted and cost-effective procedure for rapid diagnosis of thyroid lesions. The routine use of fine-needle aspiration has reduced the rate of unnecessary surgery for thyroid nodules. Objectives.—To determine the accuracy of fine-needle aspiration biopsy diagnosis and to discuss the possible pitfalls. Design, Setting, and Participants.—Reports of 6226 fine-needle aspiration biopsies of the thyroid performed during a period of 16 years (1982–1998) were reviewed. Computerized reports of the fine-needle aspiration biopsies were sent to the physicians who performed the procedures, and clinical follow-up information regarding the patients was requested. Twenty-four clinicians participated in the study. Histologic diagnoses were available for 354 cases. The cytopathologic diagnoses were correlated with the histologic findings or clinical outcomes. Results.—The cytologic diagnoses were as follows: 210 (3.4%) malignant, 450 (7.2%) suspicious, 3731 (60%) benign, and 1845 (29.5%) unsatisfactory. Most of the cases with negative or unsatisfactory aspirates were followed clinically or by repeat fine-needle aspiration. We identified 11 false-negative and 7 false-positive diagnoses. For aspirates considered sufficient for diagnosis, the sensitivity and specificity levels were 93% and 96%, respectively. Conclusions.—Fine-needle aspiration of the thyroid gland is highly accurate and has a low rate of false-negative and false-positive diagnoses. The major diagnostic problems are caused by diagnosis using a marginally adequate specimen, diagnosis of malignancy based on just 1 or 2 atypical cytologic features, or overlapping cytologic features of follicular neoplasm with those of follicular variant of papillary carcinoma.

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11

Kühne Escolà, Jordi, Simon Nagel, Christina Verez Sola, Eva Doroszewski, Hannah Jaschonek, Alexander Gutschalk, Christoph Gumbinger, and Jan C. Purrucker. "Diagnostic Accuracy in Teleneurological Stroke Consultations." Journal of Clinical Medicine 10, no. 6 (March 11, 2021): 1170. http://dx.doi.org/10.3390/jcm10061170.

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Background: The accuracy of diagnosing acute cerebrovascular disease via a teleneurology service and the characteristics of misdiagnosed patients are insufficiently known. Methods: A random sample (n = 1500) of all teleneurological consultations conducted between July 2015 and December 2017 was screened. Teleneurological diagnosis and hospital discharge diagnosis were compared. Diagnoses were then grouped into two main categories: cerebrovascular disease (CVD) and noncerebrovascular disease. Test characteristics were calculated. Results: Out of 1078 consultations, 52% (n = 561) had a final diagnosis of CVD. Patients with CVD could be accurately identified via teleneurological consultation (sensitivity 95.2%, 95% CI 93.2–96.8), but we observed a tendency towards false-positive diagnosis (specificity 77.4%, 95% CI 73.6–80.8). Characteristics of patients with a false-negative CVD diagnosis were similar to those of patients with a true-positive diagnosis, but patients with a false-negative CVD diagnosis had ischemic heart disease less frequently. In retrospect, one patient would have been considered a candidate for intravenous thrombolysis (0.2%). Conclusions: Teleneurological consultations are accurate for identifying patients with CVD, and there is a very low rate of missed candidates for thrombolysis. Apart from a lower prevalence of ischemic heart disease, characteristics of “stroke chameleons” were similar to those of correctly identified CVD patients.

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12

Del-Ben, Cristina Marta, Jaime Eduardo Cecílio Hallak, Alcion Sponholz Jr, João Mazzoncini de Azevedo Marques, Cybelli Morelo Labate, José Onildo Betioli Contel, and Antonio Waldo Zuardi. "Accuracy of psychiatric diagnosis performed under indirect supervision." Revista Brasileira de Psiquiatria 27, no. 1 (March 2005): 58–62. http://dx.doi.org/10.1590/s1516-44462005000100013.

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This work aimed at comparing the accuracy of the psychiatric diagnoses made under indirect supervision to the diagnoses obtained through Structured Clinical Interview for DSM-III-R (SCID). The study was conducted in 3 university services (outpatient, inpatient and emergency). Data from the emergency service were collected 3 years later, after changes in the training process of the medical staff in psychiatric diagnosis. The sensitivity for Major Depression (outpatient 10.0%; inpatients 60.0%, emergency 90.0%) and Schizophrenia (44.4%; 55.0%; 80.0%) improved over time. The reliability was poor in the outpatient service (Kw = 0.18), and at admission to the inpatient service (Kw = 0.38). The diagnosis elaborated in the discharge of the inpatient service (Kw = 0.55) and in the emergency service (Kw = 0.63) was good. Systematic training of supervisors and residents in operational diagnostic criteria increased the accuracy of psychiatric diagnoses elaborated under indirect supervision, although excellent reliability was not achieved.

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13

Atarashi, Hirotsugu, Yasumi Endoh, Masaaki Yashima, Takao Katoh, Hiroshi Kishida, and Hirokazu Hayakawa. "Accuracy in computer diagnosis of electrocardiogram." Japanese Journal of Electrocardiology 12, no. 2 (1992): 153–60. http://dx.doi.org/10.5105/jse.12.153.

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14

Tene Koyazo, Jacques, Moise Avoci Ugwiri, Aimé Lay-Ekuakille, Maria Fazio, Massimo Villari, and Consolatina Liguori. "Collaborative systems for telemedicine diagnosis accuracy." ACTA IMEKO 10, no. 3 (September 30, 2021): 192. http://dx.doi.org/10.21014/acta_imeko.v10i3.1133.

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The transmission of medical data and the possibility for distant healthcare structures to share experiments about a given medical case raises several conceptual and technical questions. Good remote healthcare monitoring deals with more problems in personalized heath data processing compared to the traditional methods nowadays used in several parts of hospitals in the world. The adoption of telemedicine in the healthcare sector has significantly changed medical collaboration. However, to provide good telemedicine services through new technologies such as cloud computing, cloud storage, and so on, a suitable and adaptable framework should be designed. Moreover, in the chain of medical information exchange, between requesting agencies, including physicians, a secure and collaborative platform enhanced the decision-making process. This paper provides an in-depth literature review on the interaction that telemedicine has with cloud-based computing. On the other hand, the paper proposes a framework that can allow various research organizations, healthcare sectors, and government agencies to log data, develop collaborative analysis, and support decision-making. The electrocardiogram (ECG) and electroencephalogram EEG case studies demonstrate the benefit of the proposed approach in data reduction and high-fidelity signal processing to a local level; this can make possible the extracted characteristic features to be communicated to the cloud database.

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15

Imai, K., T. Suzuki, K. Kurokawa, K. Watanabe, and H. Yamanaka. "Accuracy of Diagnosis for Prostate Cancer." Aktuelle Urologie 27, S 1 (April 1996): 23–24. http://dx.doi.org/10.1055/s-2008-1055641.

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16

Mölsä, P. K., L. Paljärvi, U. K. Rinne, and E. Säkö. "Accuracy of clinical diagnosis in dementia." Acta Neurologica Scandinavica 69, S98 (January 29, 2009): 232–33. http://dx.doi.org/10.1111/j.1600-0404.1984.tb02453.x.

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17

BRINKMAN, JAN A. "Verbal protocol accuracy in fault diagnosis." Ergonomics 36, no. 11 (November 1993): 1381–97. http://dx.doi.org/10.1080/00140139308968007.

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18

Levine, M. S., A. J. Macones, and I. Laufer. "Candida esophagitis: accuracy of radiographic diagnosis." Radiology 154, no. 3 (March 1985): 581–87. http://dx.doi.org/10.1148/radiology.154.3.3969456.

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19

Lopes Monteiro da Cruz, Dina de Almeida. "Accuracy of Nursing Diagnosis: Interrater Agreement." International Journal of Nursing Terminologies and Classifications 14, s4 (October 2003): 47. http://dx.doi.org/10.1111/j.1744-618x.2003.046_3.x.

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20

McClure, Suzanne. "Unlikely to Improve Accuracy of Diagnosis." American Journal of Clinical Pathology 88, no. 4 (October 1, 1987): 541. http://dx.doi.org/10.1093/ajcp/88.4.541a.

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21

Zweifler, Richard M., Danny York, Tha Tha U, Jorge E. Mendizabal, and John F. Rothrock. "Accuracy of paramedic diagnosis of stroke." Journal of Stroke and Cerebrovascular Diseases 7, no. 6 (November 1998): 446–48. http://dx.doi.org/10.1016/s1052-3057(98)80130-4.

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22

Lima, Tania Mara Assis, Denise Utsch Gonçalves, Lucas V. Gonçalves, Paulo Augusto C. Reis, Angela Beatriz S. Lana, and Fernando F. Guimarães. "Flexible nasolaryngoscopy accuracy in laryngomalacia diagnosis." Brazilian Journal of Otorhinolaryngology 74, no. 1 (January 2008): 29–32. http://dx.doi.org/10.1016/s1808-8694(15)30747-3.

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23

Rolfe, Heidi. "Accuracy in skin cancer diagnosis: Response." Australasian Journal of Dermatology 53, no. 3 (August 2012): 241. http://dx.doi.org/10.1111/j.1440-0960.2012.00935.x.

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24

Mettler, L., DV Shukla, and T. Schollmeyer. "Accuracy of laparoscopic diagnosis of endometriosis." Journal of the American Association of Gynecologic Laparoscopists 10, no. 3 (August 2003): S11. http://dx.doi.org/10.1016/s1074-3804(03)80032-4.

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25

Shashy, R. "Accuracy of the chronic sinusitis diagnosis." Otolaryngology - Head and Neck Surgery 129, no. 2 (August 2003): P113—P114. http://dx.doi.org/10.1016/s0194-5998(03)00983-5.

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26

Margo, Curtis E. "Eyelid tumors: accuracy of clinical diagnosis." American Journal of Ophthalmology 128, no. 5 (November 1999): 635–36. http://dx.doi.org/10.1016/s0002-9394(99)00302-5.

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27

SCHONFELD, AMY ROTHMAN. "MRI Improves Accuracy of Spondyloarthritis Diagnosis." Internal Medicine News 44, no. 8 (May 2011): 19. http://dx.doi.org/10.1016/s1097-8690(11)70392-4.

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28

Atarashi, Hirotsugu. "Accuracy in computer diagnosis of electrocardiogram." Journal of Nippon Medical School 60, no. 1 (1993): 67–73. http://dx.doi.org/10.1272/jnms1923.60.67.

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29

Cockerell, Clay J., and Lori D. Stetler. "Accuracy in Diagnosis of Lymphomatoid Papulosis." American Journal of Dermatopathology 13, no. 1 (February 1991): 20–25. http://dx.doi.org/10.1097/00000372-199102000-00005.

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30

Kasner, Scott E., and Stephen E. Kimmel. "Accuracy of Initial Stroke Subtype Diagnosis." Cerebrovascular Diseases 10, no. 1 (2000): 18–24. http://dx.doi.org/10.1159/000016020.

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31

Rosado, Barbara. "Accuracy of Computer Diagnosis of Melanoma." Archives of Dermatology 139, no. 3 (March 1, 2003): 361. http://dx.doi.org/10.1001/archderm.139.3.361.

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32

Hansen, Louise Houlberg, and Søren Mikkelsen. "Ischaemic Heart Disease: Accuracy of the Prehospital Diagnosis—A Retrospective Study." Emergency Medicine International 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/754269.

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Purpose. Correct prehospital diagnosis of ischaemic heart disease (IHD) may accelerate and improve the treatment. We sought to evaluate the accuracy of prehospital diagnoses of ischemic heart diseases assigned by physicians.Methods. The Mobile Emergency Care Unit (MECU) in Odense, Denmark, services a population of 260.000. All admissions in 2009 concerning patients diagnosed in the IHD category were assessed. Outcome and diagnosis of each patient were manually validated in accordance to the final diagnosis established following admission to hospital, using the discharge summary from the relevant department as reference.Results. 428 MECU runs with a prehospital diagnosis of IHD were registered. 422 of these were included in the study and 354 of those patients were suitable for this analysis. 73,4% of the patients hospitalized with a prehospital diagnosis of IHD were initially admitted to the relevant ward. Of these patients, 40,0% had their preliminary diagnosis of IHD confirmed. 14,1% of all patients admitted to the hospital were diagnosed with nonheart conditions. Preliminary diagnoses of STEMI had an accuracy of 87,5%.Conclusions. The preliminary IHD diagnoses assigned by the MECU physicians were acceptable. In case of STEMI patients the diagnostic accuracy was excellent. In this study there was an apparent overtriage.

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Pongsuvareeyakul, Tip, Surapan Khunamornpong, Jongkolnee Settakorn, Kornkanok Sukpan, Prapaporn Suprasert, and Sumalee Siriaunkgul. "Accuracy of Frozen-Section Diagnosis of Ovarian Mucinous Tumors." International Journal of Gynecologic Cancer 22, no. 3 (March 2012): 400–406. http://dx.doi.org/10.1097/igc.0b013e31823dc328.

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ObjectiveThe objective of the study was to evaluate the diagnostic accuracy of intraoperative frozen sections of ovarian mucinous tumors and to identify the features associated with an inaccurate diagnosis.MethodsCases of ovarian mucinous tumors (benign, low malignant potential [LMP] or borderline, primary malignant, and metastatic) diagnosed by frozen section or final histology were recruited. Frozen-section diagnoses were compared with the final histologic diagnoses. Possible variables associated with diagnostic discrepancy were analyzed.ResultsA comparison of the diagnoses was done in 195 cases (102 benign, 61 LMP, 18 primary malignant, and 14 metastatic). Diagnostic agreement was observed in 164 cases (84.1%) and discrepancy in 31 cases (15.9%). The sensitivity of frozen-section diagnosis was low in LMP (67.2%) and malignant tumors (55.6%). The specificity was the lowest in the benign category (78.5%). The positive predictive values of all categories were less than 90% (range, 83.3%–85.7%). Diagnostic discrepancy was associated with tumor size of greater than 13 cm (P = 0.019) and the number of frozen sections of 4 or more (P = 0.035). However, in a multivariate analysis, there was no independent predictor of diagnostic discrepancy. The number of frozen sections 4 or more was strongly associated with tumor size of greater than 13 cm (P = 0.004).ConclusionsThe sensitivity of frozen-section diagnosis of LMP and malignant mucinous tumors was low. The inaccuracy of a frozen-section diagnosis of ovarian mucinous tumors may be related to a tumor size of greater than 13 cm. Increasing the number of intraoperative samples over 3 sections per case may not effectively increase the accuracy of frozen-section diagnosis in mucinous tumors.

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34

Siau, Keith, Rosanna Cannatelli, Giulio Antonelli, Samuel C. L. Smith, Uday N. Shivaji, Subrata Ghosh, John R. Saltzman, Cesare Hassan, and Marietta Iacucci. "Training methods in optical diagnosis and characterization of colorectal polyps: a systematic review and meta-analysis." Endoscopy International Open 09, no. 05 (April 22, 2021): E716—E726. http://dx.doi.org/10.1055/a-1381-7181.

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Abstract Background and study aims Correct optical diagnosis of colorectal polyps is crucial to implement a resect and discard strategy. Training methods have been proposed to reach recommended optical diagnosis thresholds. The aim of our study was to present a systematic review and meta-analysis on optical diagnosis training. Methods PubMed/Medline and Cochrane databases were searched between 1980 and October 2019 for studies reporting outcomes on optical diagnosis training of colorectal polyps. The primary outcome was optical diagnosis accuracy compared to histological analysis pre-training and post-training intervention. Subgroup analyses of experienced/trainee endoscopists, training methods, and small/diminutive polyps were included. Results Overall, 16 studies met inclusion criteria, analyzing the impact of training on 179 endoscopists. Pre-training accuracy was 70.3 % (6416/9131 correct diagnoses) whereas post-training accuracy was 81.6 % (7416/9213 correct diagnoses) (risk ratio [RR] 1.17; 95 % confidence interval [CI]: 1.09–1.24, P < 0.001). In experienced endoscopists, accuracy improved from 69.8 % (3771/5403 correct diagnoses) to 82.4 % (4521/5485 correct diagnoses) (RR 1.20; 95 % CI: 1.11–1.29, P < 0.001). Among trainees, accuracy improved from 69.6 % (2645/3803 correct diagnoses) to 78.8 % (2995/3803 correct diagnoses) (RR 1.14; 95 % CI 1.06–1.24, P < 0.001). In the small/diminutive polyp subgroup, accuracy improved from 68.1 % (3549/5214 correct diagnoses) to 77.1 % (4022/5214 correct diagnoses) in (RR 1.16 95 % CI 1.08–1.24 P < 0.001). On meta-regression analysis, the improvement in accuracy did not differ between computerized vs. didactic training approaches for experienced (P = 0.792) and trainee endoscopists (P = 0.312). Conclusions Optical diagnosis training is effective in improving accuracy of histology prediction in colorectal polyps. Didactic and computer-based training show comparable effectiveness in improving diagnostic accuracy.

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35

Mohammed Alanazi, Faheem, Ali Alyami, and Najd Mujawwil Alanazi. "Discrepancy between Hospital Discharge Diagnosis and Emergency Department Admission Diagnosis." Journal of Pharmaceutical Research International 35, no. 1 (January 29, 2023): 18–23. http://dx.doi.org/10.9734/jpri/2023/v35i17299.

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The objective is to investigate the matching between emergency department and discharge diagnosis as well as how investigations affect accurately of emergency department diagnosis. The study was conducted retrospectively over a six-month period. Setting: The emergency department Department at king Saud medical (KSMC), including four in-patient specialties: orthopedics, surgery, and medicine. Subjects: All patients that entered the emergency room during the study period. The correlation degree of hospital discharge diagnosis and admission diagnosis serve as the primary outcome markers. Results: 72% of diagnoses at admission completely or partially matched diagnoses at discharge. Young people, and traumatized cases had significantly superior diagnosis accuracy, according to data. The diagnostic accuracy was impacted by the specialization department and the investigations done. In conclusion, the physical examination and history are still the two most important diagnostic techniques employed in the emergency room. Simple tests available in the ER were frequently ineffective at improving diagnostic accuracy.

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Pitner, Hilary, Charles Elmaraghy, Beth Fischer, Amanda Onwuka, Andrew Rabe, and Patrick Walz. "Diagnostic Accuracy of Midline Pediatric Neck Masses." Otolaryngology–Head and Neck Surgery 160, no. 6 (February 5, 2019): 1111–17. http://dx.doi.org/10.1177/0194599819827845.

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Objective To assess clinical evaluation, ultrasound, and previously published predictive score at preoperatively diagnosing midline neck masses and demographic or clinical associations that aid in differentiation of thyroglossal duct and dermoid cysts. Study Design Retrospective chart review. Setting Tertiary care children’s hospital. Subjects Patients <18 years undergoing primary midline neck mass surgery with histopathologic diagnosis of thyroglossal duct or dermoid cyst who had preoperative ultrasound performed were included. Methods An electronic medical record query generated 142 patients whose histopathologic diagnosis was thyroglossal duct cysts (TGDCs) or dermoid cysts (DCs). Charts were reviewed for demographic and clinical features. A radiologist blindly reviewed patients’ ultrasounds for SIST (septae + irregular walls + solid components = thyroglossal) score components. Each patient received 3 preoperative diagnoses: clinical, ultrasound, and SIST. Statistical analyses were conducted to determine association of demographic, clinical, or radiographic variables with diagnoses. Specificity, sensitivity, and predictive values were evaluated for each candidate diagnosis. Results There were 83 TGDCs and 59 DCs. Tenderness, infection history, depth relative to strap muscles, and SIST components were more common among TGDCs. Sensitivity and positive and negative predictive values surpassed 63% for each diagnostic modality. SIST score outperformed other diagnostic modalities with sensitivity, positive predictive value, and negative predictive value of 84%, 91%, and 81%, respectively. Clinical and ultrasound assessments were largely inconclusive for dermoid cysts, but SIST correctly identified 89% of DCs. Conclusion SIST score was the most accurate predictor of pediatric midline neck masses. Clinical and radiographic findings may help guide preoperative diagnosis, although further evaluation is required to develop more efficacious diagnostic tools.

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37

Whiting, Penny, Roger Harbord, Isabel de Salis, Matthias Egger, and Jonathan Sterne. "Evidence-based diagnosis." Journal of Health Services Research & Policy 13, no. 3_suppl (October 2008): 57–63. http://dx.doi.org/10.1258/jhsrp.2008.008025.

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Making an accurate diagnosis is essential to ensure that a patient receives appropriate treatment and correct information regarding their prognosis. Characteristics of diagnostic tests are quantified in test accuracy studies, but many such studies have methodological flaws. The HSRC evidence-based diagnosis programme has focused on methods for systematic reviews of test accuracy studies, and the wider context in which tests are ordered and interpreted. We carried out a range of projects relating to literature searching, quality assessment, meta-analysis, presentation of results, and interactions between doctors and patients during the diagnostic process. We have shown that systematic reviews of test accuracy studies should search a range of databases and that current diagnostic filters do not have sufficient accuracy to be used in test accuracy reviews. Summary quality scores should not be used in test accuracy reviews; the Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Reviews (QUADAS) tool for assessing test accuracy studies is acceptable for quality assessment. We have shown that the hierarchical summary receiver operating characteristic (HSROC) and bivariate models for meta-analysis of test accuracy are statistically equivalent in many circumstances, and have developed an add-on module for the statistical software package Stata that enables these statistically rigorous models to be fitted by those without expert statistical knowledge. Three areas that would benefit from further research are literature searching, synthesis of results from individual patient data and presentation of results.

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38

Gnepp, Douglas R., Willa Rae Rader, Stewart F. Cramer, Linda L. Cook, and James Sciubba. "Accuracy of Frozen Section Diagnosis of the Salivary Gland." Otolaryngology–Head and Neck Surgery 96, no. 4 (April 1987): 325–30. http://dx.doi.org/10.1177/019459988709600404.

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Three hundred and one salivary gland lesions (162 benign, 72 malignant, and 67 benign non-neoplastic) of 677 cases were evaluated by use of intraoperative frozen sections by 66 pathologists. In seven patients, the diagnosis was deferred for permanent sections. In four cases (1.3%), the diagnosis at permanent section changed from one category of benign tumor to another, and in five cases (1.7%), from one category of malignant tumor to another. In four tumors, a frozen section diagnosis of benign was changed to malignant on permanent sectioning; all four involved acinic cell carcinomas. Only two tumors were incorrectly diagnosed as malignant. We conclude that diagnoses of most salivary gland lesions based on frozen section examination are reliable and accurate. However, the literature does indicate that caution should be exercised when malignant tumors are dealt with.

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39

Zou, Guangyong. "From Diagnostic Accuracy to Accurate Diagnosis: Interpreting a Test Result with Confidence." Medical Decision Making 24, no. 3 (June 2004): 313–18. http://dx.doi.org/10.1177/0272989x04265483.

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40

Kang, Swan, Mohammad Dehabadi, Dawn A. Sim, Peter B. M. Thomas, Doris Appiah Ewusi, and Daniel Ezra. "Accuracy of periocular lesion assessment using telemedicine." BMJ Health & Care Informatics 28, no. 1 (January 2021): e100287. http://dx.doi.org/10.1136/bmjhci-2020-100287.

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ObjectivesTo assess the agreement in diagnosis and management plans reached between clinicians reviewing eyelid lesions remotely and in face-to-face clinics.MethodsIn this single-centre observational case series, data were prospectively collected on 50 consecutive adults referred with eyelid lesions suitable to be seen by a nurse. A proforma was completed to gather salient information. A nurse specialist saw patients in face-to-face clinics and collected information using the proforma, devising a diagnosis and management plan. Photographs of the eyelid lesions were taken by a medical photographer. A subsequent remote review was completed by an oculoplastic consultant using the proforma information and photographs in the absence of the patient. The diagnosis and management plan constructed by the nurse specialist were compared with those reached by the consultant.ResultsComplete data were available for 44 consecutive cases. There was an overall 91% agreement (40 cases out of 44) between the diagnoses reached by the nurse specialist, and the remote reviewer; kappa coefficient 0.88 (95% CI 0.76 to 0.99). There was an overall 82% agreement (36 out of 44 cases) in the management plans devised by the nurse-led clinic and remote reviewer; kappa coefficient 0.74 (95% CI 0.58 to 0.90). The average time taken for a remote reviewer to reach a diagnosis and management plan was 1 min and 20 s.ConclusionsThis study evaluated the feasibility of assessing eyelid lesions using asynchronous telemedicine. There was overall a high rate of concordance in the diagnosis reached, and management devised between the clinic and remote review.

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Eich, CS, JG Whitehair, SD Moroff, and LA Heeb. "The accuracy of intraoperative cytopathological diagnosis compared with conventional histopathological diagnosis." Journal of the American Animal Hospital Association 36, no. 1 (January 1, 2000): 16–18. http://dx.doi.org/10.5326/15473317-36-1-16.

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To determine the accuracy of intraoperative cytopathological diagnosis compared with conventional histopathological diagnosis, the authors obtained 100 specimens from masses of various organ systems chosen randomly from 65 dogs, 30 cats, and five exotic animals. Of the 100 specimens, a specific diagnosis was obtained in 42%, the correct pathological process (i.e., mesenchymal neoplasia, epithelial neoplasia, round cell neoplasia, or inflammation) was identified in 41%, in 1% the diagnosis was deferred, and in 16% an incorrect diagnosis was obtained. The overall accuracy rate of intraoperative cytopathological examination was 83%, which increased to 90% by the exclusion of splenic masses. The accuracy rate of diagnosing neoplasia was 87%, with a sensitivity of 89% and a specificity of 100%. Intraoperative cytopathological examination is an accurate diagnostic method with good sensitivity and specificity for the identification of neoplasia.

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Tangjitgamol, S., S. Jesadapatrakul, S. Manusirivithaya, and C. Sheanakul. "Accuracy of frozen section in diagnosis of ovarian mass." International Journal of Gynecologic Cancer 14, no. 2 (2004): 212–19. http://dx.doi.org/10.1136/ijgc-00009577-200403000-00005.

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To determine the accuracy of frozen section according to the status of malignancy and the histologic cell type, we reviewed the frozen and permanent pathologic reports of 212 resected ovarian masses in our hospital. The accuracy, sensitivity, specificity, positive, and negative predictive value of frozen section were studied. The overall accuracy to determine the status of malignancy was 90.9%. Sensitivity of the test was highest in the benign groups at 99.1% and lowest in the borderline groups at 50%. All inaccurate diagnoses were in the common epithelial groups. Problems in diagnosis of mucinous tumors and borderline tumors were striking. The accuracy of the test for histologic diagnosis was 91.9%. Most cases of the incorrect diagnosis (81.3%) were common epithelial tumors. In conclusion, the accuracy of frozen section in the diagnosis of ovarian mass was generally high with a few exceptions in large tumors, mucinous, or borderline tumors that yielded lower accuracy, sensitivity, specificity, and positive predictive value. We encourage both the surgeons and the pathologists to be cautious of these limitations. Additional number of frozen section taken for a mass larger than 10 cm may minimize the error in large tumors to some extent.

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Alshargi, Ali Omar, Swedi Essa Al-sallami, Hamed Arif Mohammad Hejazi, and Abdulmaajid Ibrahim O. Alahdal. "Accuracy of ultrasound in diagnosis of cholecystitis." Egyptian Journal of Hospital Medicine 73, no. 7 (October 1, 2018): 7146–52. http://dx.doi.org/10.21608/ejhm.2018.17604.

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Wagner, Jason M., Kenneth S. Lee, Humberto Rosas, and Mark A. Kliewer. "Accuracy of Sonographic Diagnosis of Superficial Masses." Journal of Ultrasound in Medicine 32, no. 8 (August 2013): 1443–50. http://dx.doi.org/10.7863/ultra.32.8.1443.

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Ozsahin, Ilker, Boran Sekeroglu, Pwadubashiyi Coston Pwavodi, and Greta S. P. Mok. "High-accuracy Automated Diagnosis of Parkinson's Disease." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 6 (July 27, 2020): 688–94. http://dx.doi.org/10.2174/1573405615666190620113607.

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Purpose: Parkinson's disease (PD), which is the second most common neurodegenerative disease following Alzheimer’s disease, can be diagnosed clinically when about 70% of the dopaminergic neurons are lost and symptoms are noticed. Neuroimaging methods such as single photon emission computed tomography have become useful tools in vivo to assess dopamine transporters (DATs) in the striatal region. However, inter- and intra-reader variability of construing the images might result in misdiagnosis. To overcome the challenges posed by classification of the disease, image preparation techniques and a back propagation neural network (BPNN) have been proposed. The aim of this study is to show that the proposed method can be used for the classification of PD with high accuracy. Methods: In this study, we used basic image preparation techniques and a BPNN on DAT imaging datasets from the Parkinson’s Progression Markers Initiative. 1,334 PD and 212 normal control (NC) subjects were included. In the image preparation phase, adaptive histogram equalization was applied to the cropped images, followed by image binarization. Then, the mass-difference method was applied to separate the regions of interest with similar values. Finally, the binarized images were subtracted from the original images, and the average pixel per node approach was applied to the images to minimize the inputs. In the BPNN phase, 400 input neurons and 2 output neurons were used. The dataset was divided into three sets: training, validation, and test. The BPNN was trained several times in order to obtain the optimum values. Results: The use of 40 hidden neurons, a learning rate of 0.00079, and a momentum factor of 0.90 produced superior results and were applied in the final BPNN architecture. The tolerance value used was 0.80. Uniquely, we found the sensitivity, specificity, and accuracy for PD vs. NC classification to be 99.7%, 99.2%, 99.6%, respectively. To the best of our knowledge, this is the highest accuracy value achieved in the existing literature. Our method increases computational speed together with improved performance. Conclusion: We have shown that effective image processing methods and the use of BPNN can successfully be applied to PD datasets to accurately determine any abnormalities in DATs. Using the shallow neural network, this procedure requires less processing time compared to other methods, and its accuracy, sensitivity, and specificity are reliable. However, further studies are needed to establish a prediction method for the preclinical and prodromal stages of the disease.

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Tomita, Masato, Noriaki Miyata, Yoshihiro Nozaki, Kenji Kumagai, Tomayoshi Hayashi, Kuniko Abe, Naoe Kinoshita, and Hiroyuki Shindo. "Accuracy of Cytological Diagnosis in Musculoskeletal Tumors." Orthopedics & Traumatology 61, no. 2 (2012): 173–75. http://dx.doi.org/10.5035/nishiseisai.61.173.

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Fritz, Mark A., Michael J. Persky, Yixin Fang, C. Blake Simpson, Milan R. Amin, Lee M. Akst, and Gregory N. Postma. "The Accuracy of the Laryngopharyngeal Reflux Diagnosis." Otolaryngology–Head and Neck Surgery 155, no. 4 (July 22, 2016): 629–34. http://dx.doi.org/10.1177/0194599816655143.

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Benseñor, Isabela Martins, Ana Luísa Garcia Calich, André Russowsky Brunoni, Fábio Ferreira do Espírito-Santo, Renato Lendimuth Mancini, Luciano Ferreira Drager, and Paulo Andrade Lotufo. "Accuracy of anemia diagnosis by physical examination." Sao Paulo Medical Journal 125, no. 3 (May 2007): 170–73. http://dx.doi.org/10.1590/s1516-31802007000300008.

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CONTEXT AND OBJECTIVES: Quantification of clinical signs such as the presence or absence of pallor at clinical examination is a key step for making diagnoses. The aim was, firstly, to evaluate two methods for anemia diagnosis by physical examination: four-level evaluation (crosses method: +/++/+++/++++) and estimated hemoglobin values, both performed by medical students and staff physicians; and secondly, to investigate whether there was any improvement in assessment accuracy according to the number of years in clinical practice. DESIGN AND SETTING: Forty-four randomly selected physicians and medical students in a tertiary care teaching hospital completed a physical examination on five patients with mild to severe anemia. METHODS: The observers used four-level evaluation and also predicted the hemoglobin level. Both methods were compared with the real hemoglobin value as the gold standard. RESULTS: The mean estimated hemoglobin value correlated better with the real hemoglobin values than did the four-level evaluation method, for attending physicians, residents and students (Spearman's correlation coefficients, respectively: 1.0, 1.0 and 0.9 for guessed hemoglobin and -0.8, -0.8 and -0.7 for the four-level evaluation method). There were no differences in the mean "guessed" hemoglobin values from attending physicians, residents and students. However, the correlation between guessed hemoglobin value and the four-level method was positive for attending physicians, thus suggesting some kind of improvement with time (p = 0.04). CONCLUSIONS: This study showed that estimated hemoglobin was more accurate than evaluation by the four-level method. The number of years in clinical practice did not improve the accuracy of clinical examination for anemia.

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Nimmagadda, S. R. "Increasing the Accuracy of Peanut Allergy Diagnosis." AAP Grand Rounds 28, no. 2 (August 1, 2012): 15. http://dx.doi.org/10.1542/gr.28-2-15.

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K, Venugopal, Pratap Balakrishna, and Nikshita N. "FNAC ACCURACY IN DIAGNOSIS OF BREAST LESIONS." Journal of Evolution of Medical and Dental Sciences 3, no. 12 (March 20, 2014): 3062–68. http://dx.doi.org/10.14260/jemds/2014/2244.

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