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Articoli di riviste sul tema "Scores"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 29 luglio 2024

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1

Taylor, William E., Christopher J. Cassady, Steven M. Lonergan, Ben D. Peyer e Kenneth J. Stalder. "PSVII-11 Utilizing digital images to evaluate accuracy and repeatability of body condition score in cull sows". Journal of Animal Science 98, Supplement_3 (2 novembre 2020): 169. http://dx.doi.org/10.1093/jas/skaa054.300.

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Abstract The objective of this study evaluated the accuracy and repeatability when determining cull sow body condition scores (BCS) evaluated by scorers utilizing digital images. Participants (n=6) were selected based on previous BCS scoring on live sows. Group standards were established utilizing the scores from two participants with extensive experience BCS sows. Other scorers were not provided training before they scored images and no “scorer calibration” activity occurred to ensure that each scorer was observing similar traits when applying BCS. Two separate groups of sows were scored and video images for each sow were collected and stored for evaluation. The images were recorded as a convenience sample from a cooperating sow abattoir. The cull sow video images were assigned a BCS using an 8-point scale (1,1+,2-,2,3,4,5,NS). Scores from the lower portion of the scale were classified (+ and -) to provide a more specific BCS difference evaluation among sows that were thin. When scorers evaluated the sows (n=165) from the first group, they scored the BC for each sow. Additionally, a random group of sows (n=40) images were selected to be scored a second time by the same scorer. Repeatability (inter- and intra- scorer), individual bias, group bias, and group deviation from standard’s mean BCS were calculated. Data from the first sow group showed that relative to the standard’s mean scores, participants overestimated BCS by .41 (P=0.0001). A similarly selected second set of cull sow images obtained from the same abattoir was recorded. The second group included more sow images (n=220) and a greater number images that were scored a second time(n=55) in order to calculate the repeatability values. The same measures described for group 1 were calculated. After data analysis from the second sow group, it was again noted that participants overestimated BCS by 0.1 (P=0.0009).

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2

Liu, Vincent. "Keeping Score of Severity Scores". Critical Care Medicine 44, n. 3 (marzo 2016): 639–40. http://dx.doi.org/10.1097/ccm.0000000000001501.

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3

Ferreira, Daniel. "Risk stratification after acute coronary syndromes: Scores, scores and yet another score". Revista Portuguesa de Cardiologia (English Edition) 36, n. 2 (febbraio 2017): 85–87. http://dx.doi.org/10.1016/j.repce.2017.02.004.

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Ferreira, Daniel. "Risk stratification after acute coronary syndromes: Scores, scores and yet another score". Revista Portuguesa de Cardiologia 36, n. 2 (febbraio 2017): 85–87. http://dx.doi.org/10.1016/j.repc.2016.12.001.

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Caplan, Louis R. "Scores of Scores". JAMA Neurology 70, n. 2 (1 febbraio 2013): 252. http://dx.doi.org/10.1001/jamaneurol.2013.1144.

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6

Zuvich, Miranda L., Lane K. Giess e R. Mark Enns. "15 Evaluating Repeatability and Scorer Differences When Assigning Feet and Legs Scores in Simmental Heifers". Journal of Animal Science 100, Supplement_4 (22 ottobre 2022): 8–9. http://dx.doi.org/10.1093/jas/skac313.011.

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Abstract The objective of this paper was to determine the relationship and repeatability of between-evaluator scores for claw set (CS), hoof angle (HA), and rear leg side view (RSV). Repeatability of feet and legs scoring has implications on scorer bias and subsequently on the estimation of genetic parameters, expected progeny differences (EPD), and accuracy. Data were collected on 112 13-15-month-old Simmental heifers by 4 evaluators, where scorer 1 (S1) was a trained and experienced evaluator and subsequently trained scorers 2 (S2), 3 (S3), and 4 (S4) for approximately 20 minutes before all 4 evaluators sequentially scored every female at the same time. Training was conducted using feet and leg rubrics provided by the American Simmental Association (ASA) on a 1-9 scale, with 5 being optimum for each trait. Scorers were provided an online evaluation instrument, requiring responses for each evaluator for each animal, and included identification, CS, HA, and RSV. Pearson correlations were computed pairwise, with each new scorer compared to the trained scorer. For partial linear regression, the three new evaluators were fit individually to S1. Pearson correlation values ranged from 0.088 to 0.397. The correlation between S1 and S3 was the strongest for all traits. The weakest correlation of 0.088 was in rear leg side view between S1 and S2. Partial linear regression adjusted R-squared values ranged from 0.00 to 0.15. The correlation between S1 and S3 was the strongest for all traits. The weakest correlation of 0.00 was in rear leg side view between S1 and S2. The correlations between S1 and S2 was significant for CS and HA (P = 0.002, P = 0.039), but not for RSV (P = 0.355). The correlation between S1 and S3 was significant for CS, HA, and RSV [P = 1.5x10^(-5), P = 0.006, P = 0.02]. The correlation between S1 and S4 was not significant for CS, HA, and RSV (P = 0.276, P = 0.085, P = 0.223). Overall, the scores had weak correlations, which illustrates the differences in variability across scorers. The weak correlation also illustrates the need for more robust and intensive training of evaluators. Overall, while the scores are not highly correlated between evaluators, scorer bias should not affect estimation of genetic parameters long as each contemporary group is scored by one evaluator.

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7

Waterman, R. "Projected score methods for approximating conditional scores". Biometrika 83, n. 1 (1 marzo 1996): 1–13. http://dx.doi.org/10.1093/biomet/83.1.1.

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8

Gadd, Molly M. "Braden Scale Cumulative Score Versus Subscale Scores". Journal of Wound, Ostomy and Continence Nursing 41, n. 1 (2014): 86–89. http://dx.doi.org/10.1097/01.won.0000438017.83110.6c.

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9

Wunderink, Richard G. "Scores and Scores of Predicted Mortality Scores". Chest 163, n. 1 (gennaio 2023): 6–7. http://dx.doi.org/10.1016/j.chest.2022.09.009.

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10

Andrade, Chittaranjan. "Z Scores, Standard Scores, and Composite Test Scores Explained". Indian Journal of Psychological Medicine 43, n. 6 (10 ottobre 2021): 555–57. http://dx.doi.org/10.1177/02537176211046525.

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Patients may be assessed using a battery of tests where different tests yield scores in different units, where different tests have different minimum and maximum scores, and where higher or lower scores mean different things in different tests. Therefore, a composite test score cannot be obtained by simple addition or averaging of scores in the individual tests. However, if performances in individual tests are converted to Z scores, the Z scores can be added or averaged to yield a composite score that can be interpreted or processed using conventional statistical methods. This article explains in simple ways how Z scores are calculated, what the properties of Z scores are, how Z scores can be interpreted, and how Z scores can be converted into other standard scores.

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Pirzada, Shariefuddin, Muhammad Ali Khan, Zhou Guofei e Koko K. Kayibi. "On scores, losing scores and total scores in hypertournaments". Electronic Journal of Graph Theory and Applications 3, n. 1 (1 aprile 2015): 8–21. http://dx.doi.org/10.5614/ejgta.2015.3.1.2.

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Tackitt, Alaina, e Daviid Eubanks. "Resetting the Score: Scores as Measures of Learning". Journal of Writing Analytics 4, n. 1 (2020): 193–202. http://dx.doi.org/10.37514/jwa-j.2020.4.1.08.

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13

Shepherd, John A., e Glen M. Blake. "T-Scores and Z-Scores". Journal of Clinical Densitometry 10, n. 4 (ottobre 2007): 349–50. http://dx.doi.org/10.1016/j.jocd.2007.08.006.

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Carey, John J., e Miriam F. Delaney. "T-Scores and Z-Scores". Clinical Reviews in Bone and Mineral Metabolism 8, n. 3 (10 novembre 2009): 113–21. http://dx.doi.org/10.1007/s12018-009-9064-4.

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Sedgwick, P. "T scores and z scores". BMJ 341, dec30 2 (30 dicembre 2010): c7362. http://dx.doi.org/10.1136/bmj.c7362.

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Bright, A., TA Jones e MS Dawkins. "A non-intrusive method of assessing plumage condition in commercial flocks of laying hens". Animal Welfare 15, n. 2 (maggio 2006): 113–18. http://dx.doi.org/10.1017/s0962728600030190.

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AbstractStandard techniques for assessing plumage damage to hens from feather pecking typically require capture and handling. Handling of individual birds for plumage assessment is relatively easy in experimental studies; however, close inspection of individual birds in commercial flocks is less feasible because catching birds is difficult, may compromise bird welfare and affect egg production. The aim of this study was to assess a non-intrusive method for scoring plumage damage in a commercial free-ranging flock of laying hens. Plumage damage was scored within a 2 m distance of the birds, without capture or handling, using a 5-point scale for 5 body regions. The feather scores, recorded at a distance, by two independent scorers were compared (distance scores), and were then compared with feather scores recorded by a scorer who caught and handled the birds to examine the plumage damage closely (capture scores). There was a significant and positive correlation between the distance scores and the capture scores, and the mean correlation coefficient for all plumage score traits was 0.89. There was also a significant and positive correlation between scorers, and the mean correlation coefficient for all plumage score traits was 0.84. The standard deviation of the residual mean difference between scorers and between methods was less than 1 point for individual body regions and less than 1.5 points for the total body score. Large variation in feather damage within a flock and small sample size increased the standard error of the mean total feather score. When feather damage variation within flocks is low (ie little observed feather damage), the current industry standard of scoring a sample of 100 birds is likely to provide a reliable estimate of flock feather damage; however, when there is large variation within birds of a flock (ie considerable observed feather damage) ≥200 birds should be inspected to accurately monitor changes in plumage condition. The non-intrusive method of feather scoring described in this paper may be useful for commercial-scale feather pecking studies or for farmers who need to assess the plumage damage of their flocks reliably, quickly and with minimal disturbance or stress to the birds.

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Mullins, Israel L., Carissa M. Truman, Magnus R. Campler, Jeffrey M. Bewley e Joao H. C. Costa. "Validation of a Commercial Automated Body Condition Scoring System on a Commercial Dairy Farm". Animals 9, n. 6 (29 maggio 2019): 287. http://dx.doi.org/10.3390/ani9060287.

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Body condition scoring (BCS) is the management practice of assessing body reserves of individual animals by visual or tactile estimation of subcutaneous fat and muscle. Both high and low BCS can negatively impact milk production, disease, and reproduction. Visual or tactile estimation of subcutaneous fat reserves in dairy cattle relies on their body shape or thickness of fat layers and muscle on key areas of the body. Although manual BCS has proven beneficial, consistent qualitative scoring can be difficult to implement. The desirable BCS range for dairy cows varies within lactation and should be monitored at multiple time points throughout lactation for the most impact, a practice that can be hard to implement. However, a commercial automatic BCS camera is currently available for dairy cattle (DeLaval Body Condition Scoring, BCS DeLaval International AB, Tumba, Sweden). The objective of this study was to validate the implementation of an automated BCS system in a commercial setting and compare agreement of the automated body condition scores with conventional manual scoring. The study was conducted on a commercial farm in Indiana, USA, in April 2017. Three trained staff members scored 343 cows manually using a 1 to 5 BCS scale, with 0.25 increments. Pearson’s correlations (0.85, scorer 1 vs. 2; 0.87, scorer 2 vs. 3; and 0.86, scorer 1 vs. 3) and Cohen’s Kappa coefficients (0.62, scorer 1 vs. 2; 0.66, scorer 2 vs. 3; and 0.66, scorer 1 vs. 3) were calculated to assess interobserver reliability, with the correlations being 0.85, 0.87, and 0.86. The automated camera BCS scores were compared with the averaged manual scores. The mean BCS were 3.39 ± 0.32 and 3.27 ± 0.27 (mean ± SD) for manual and automatic camera scores, respectively. We found that the automated body condition scoring technology was strongly correlated with the manual scores, with a correlation of 0.78. The automated BCS camera system accuracy was equivalent to manual scoring, with a mean error of −0.1 BCS and within the acceptable manual error threshold of 0.25 BCS between BCS (3.00 to 3.75) but was less accurate for cows with high (>3.75) or low (<3.00) BCS scores compared to manual scorers. A Bland–Altman plot was constructed which demonstrated a bias in the high and low automated BCS scoring. The initial findings show that the BCS camera system provides accurate BCS between 3.00 to 3.75 but tends to be inaccurate at determining the magnitude of low and high BCS scores. However, the results are promising, as an automated system may encourage more producers to adopt BCS into their practices to detect early signs of BCS change for individual cattle. Future algorithm and software development is likely to increase the accuracy in automated BCS scoring.

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Zhu, Chenyang, Kai Xu, Siddhartha Chaudhuri, Renjiao Yi e Hao Zhang. "SCORES". ACM Transactions on Graphics 37, n. 6 (10 gennaio 2019): 1–14. http://dx.doi.org/10.1145/3272127.3275008.

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Mills, Mara, e Neta Alexander. "Scores". Film Quarterly 76, n. 2 (2022): 39–47. http://dx.doi.org/10.1525/fq.2022.76.2.39.

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A prominent voice in the New Disability Arts movement, Carolyn Lazard is a multidisciplinary artist rethinking captioning and other access techniques as tools for composition. Reading several of Lazard’s works and collaborations, this essay theorizes the style of captions that functions as “scores”—open-ended scripts inviting audience members to activate the work, their bodies, and each other. As performance scores, these captions instruct or otherwise engage with readers/audiences, requesting their participation in the artmaking process. Lazard’s use of “scores” in works such as A Recipe for Disaster (2018), CRIP TIME (2018), and Notes from a Panorama (2021) carefully centers Black and brown, chronically ill, and other marginalized communities. As such, it uncovers the repetitive care labor of spectatorship as a form of survival.

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Evans, Larry D. "A two-score composite program for combining standard scores". Behavior Research Methods, Instruments, & Computers 28, n. 2 (giugno 1996): 209–13. http://dx.doi.org/10.3758/bf03204767.

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21

Lim, Euijin. "Reliability of TEPS Section Scores and the Total Score". Lanaguage Research 55, S (1 novembre 2019): 51–64. http://dx.doi.org/10.30961/lr.2019.55.s.51.

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22

Farooq, Vasim, Salvatore Brugaletta e Patrick W. Serruys. "The SYNTAX Score and SYNTAX-Based Clinical Risk Scores". Seminars in Thoracic and Cardiovascular Surgery 23, n. 2 (giugno 2011): 99–105. http://dx.doi.org/10.1053/j.semtcvs.2011.08.001.

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23

White, Mark, e Kirsti Klette. "What’s in a score? Problematizing interpretations of observation scores". Studies in Educational Evaluation 77 (giugno 2023): 101238. http://dx.doi.org/10.1016/j.stueduc.2023.101238.

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Mittermaier, Marion P. "How interpolation and resolution can affect verification scores: A study based on the Fractions Skill Score". Meteorologische Zeitschrift 28, n. 3 (9 ottobre 2019): 181–92. http://dx.doi.org/10.1127/metz/2018/0890.

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Öner-Özkan, Bengi. "FUTURE TIME ORIENTATION IN ROMANTIC RELATIONSHIPS AND THE MINDING THEORY OF RELATING". Social Behavior and Personality: an international journal 32, n. 8 (1 gennaio 2004): 797–804. http://dx.doi.org/10.2224/sbp.2004.32.8.797.

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The aim of this study was to explore the differences between high and low scorers of Future Time Orientation in Romantic Relationships (FTORR) on the three subscales of the minding scale, namely Acceptance, Knowledge, and Attribution. Undergraduate students (N = 160) from Middle East Technical University were given the FTORR scale developed by Öner (2000b) together with the Minding Scale developed by Omarzu, Whalen and Harvey (2001). Results indicated an interaction effect between subscales of the minding scale and FTORR. In terms of the “Acceptance” subscale, individuals with high FTORR scores were found to score lower than were individuals who had lower FTORR scores – whereas for both “Knowledge” and “Attribution” measures, individuals with higher scores of FTORR scored higher than did individuals who had lower FTORR scores. Detailed analysis of the results and research implications were discussed.

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Chang, William H., e Christy Chuang-Stein. "Component Scores, or Total Scores over Components?" Drug Information Journal 36, n. 3 (luglio 2002): 557–64. http://dx.doi.org/10.1177/009286150203600310.

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Stepanova, M., e L. C. Thomas. "PHAB scores: proportional hazards analysis behavioural scores". Journal of the Operational Research Society 52, n. 9 (settembre 2001): 1007–16. http://dx.doi.org/10.1057/palgrave.jors.2601189.

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Karjo, Clara Herlina. "Relationship between High School National Exam Scores and College Admission Test Scores in Indonesia". International Journal of Psychosocial Rehabilitation 24, n. 5 (31 marzo 2020): 561–68. http://dx.doi.org/10.37200/ijpr/v24i5/pr201721.

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Schneer, Sonia, Eli Atar, Gill Bachar, Ran Kornowski, Dana Marcovici, Victoria Beilin, Idit Maya e Dror Dicker. "VALIDATION OF THE FRAMINGHAM AND SCORE SCORES BY “CARDIAC CTA”". European Journal of Internal Medicine 19 (maggio 2008): S7. http://dx.doi.org/10.1016/s0953-6205(08)60019-9.

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MEHRENS, WILLIAM A., e IRVIN J. LEHMANN. "Interpreting Test Scores to Clients: What Score Should One Use?" Journal of Counseling & Development 63, n. 5 (gennaio 1985): 317–20. http://dx.doi.org/10.1002/j.1556-6676.1985.tb00671.x.

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31

Bernstein, David N., Jeff R. Houck, Ronald M. Gonzalez, Danielle M. Wilbur, Richard J. Miller, David J. Mitten e Warren C. Hammert. "Preoperative PROMIS Scores Predict Postoperative PROMIS Score Improvement for Patients Undergoing Hand Surgery". HAND 15, n. 2 (3 agosto 2018): 185–93. http://dx.doi.org/10.1177/1558944718791188.

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Background: Patient-Reported Outcomes Measurement Information System (PROMIS) can be used alongside preoperative patient characteristics to set postsurgery expectations. This study aimed to analyze whether preoperative scores can predict significant postoperative PROMIS score improvement. Methods: Patients undergoing hand and wrist surgery with initial and greater than 6-month follow-up PROMIS scores were assigned to derivation or validation cohorts, separating trauma and nontrauma conditions. Receiver operating characteristic curves were calculated for the derivation cohort to determine whether preoperative PROMIS scores could predict postoperative PROMIS score improvement utilizing minimal clinically important difference principles. Results: In the nontrauma sample, patients with baseline Physical Function (PF) scores below 31.0 and Pain Interference (PI) and Depression scores above 68.2 and 62.2, respectively, improved their postoperative PROMIS scores with 95%, 96%, and 94% specificity. Patients with baseline PF scores above 52.1 and PI and Depression scores below 49.5 and 39.5, respectively, did not substantially improve their postoperative PROMIS scores with 94%, 93%, and 96% sensitivity. In the trauma sample, patients with baseline PF scores below 34.8 and PI and Depression scores above 69.2 and 62.2, respectively, each improved their postoperative PROMIS scores with 95% specificity. Patients with baseline PF scores above 52.1 and PI and Depression scores below 46.6 and 44.0, respectively, did not substantially improve their postoperative scores with 95%, 94%, and 95% sensitivity. Conclusions: Preoperative PROMIS PF, PI, and Depression scores can predict postoperative PROMIS score improvement for a select group of patients, which may help in setting expectations. Future work can help determine the level of true clinical improvement these findings represent.

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Moeller, Grace A., Courtney L. Abell, Tom P. Somrack, William O. Herring, Justin W. Holl e Kenneth J. Stalder. "213 Distribution of subjectively evaluated conformation traits in commercial growing replacement gilts". Journal of Animal Science 98, Supplement_3 (2 novembre 2020): 6–7. http://dx.doi.org/10.1093/jas/skaa054.010.

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Abstract Feet and leg/locomotor issues represent a primary reason for sow removal from U.S. breeding herds. A shift from gestation stalls (GS) to group-sow housing (GSH) is occurring the U.S. swine industry, emphasizing the importance of structural soundness in replacement gilts. The objectives of this study were 1) to determine if feet and leg/body conformation score differences exist when evaluated at approximately 50 kgs. and 100 kgs.; 2) estimate subjectively scored feet and leg/body conformation trait repeatability within and across measurement time and scorer(s); and 3) determine if the ideal feet and leg conformation traits and scores within traits are the same within GS and GSH. A total of 4494 gilts were evaluated at approximately 22 weeks of age at 3 different farms within the same production system by 4 scorers, representing varying levels of experience. The conformation traits evaluated included locomotion score (LS), front and rear leg conformation (FLC and RLC), front and rear pastern angle (FP and RP), front and rear foot position (FFP and RFP), rib shape (RS), foot size (FS) and toe size (TS). Across farms, the mean gilt BW was 83 ± 11.5 kg. Across scorers and the entire gilt population, the mean conformation scores were: LS (0.06 ± 0.16), FLC (6.1 ± 0.54), RLC (5.4 ± 0.40), FP (3.6 ± 0.62), RP (4.0 ± 0.64), FFP (4.4 ± 0.31), RFP (4.1 ± 0.30), RS (4.3 ± 0.72), FS (2.0 ± 0.35) and TS (4.8 ± 0.61). The entire gilt population was divided amongst four individual sow herds (2 GS and 2 GSH). Sow performance will be followed for three parities to determine the relationship between production and longevity within gestation housing systems and determine if the relationships are associated with one or more conformation traits and/or differences in scores within each conformation trait.

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Griffiths, Paul. "New Scores". Musical Times 128, n. 1736 (ottobre 1987): 571. http://dx.doi.org/10.2307/965404.

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Howe, Rachel, e Eithne Sheehan. "Dependency scores". Paediatric Nursing 15, n. 1 (1 febbraio 2003): 6. http://dx.doi.org/10.7748/paed.15.1.6.s14.

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Rice, Alison. "Unsettling scores". Francosphères 5, n. 2 (dicembre 2016): 197–212. http://dx.doi.org/10.3828/franc.2016.14.

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Shalom, Stephen R. "Dropping Scores". Mathematics Magazine 68, n. 3 (1 giugno 1995): 220. http://dx.doi.org/10.2307/2691423.

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FITZGERALD, JON. "SCREEN SCORES". Perfect Beat 4, n. 3 (5 ottobre 2015): 86–91. http://dx.doi.org/10.1558/prbt.v4i3.28722.

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Krakovsky, Marina. "Unsettled Scores". Scientific American 296, n. 2 (febbraio 2007): 15–18. http://dx.doi.org/10.1038/scientificamerican0207-15.

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ELDERS, W. "'SYMBOLIC SCORES'". Music and Letters 77, n. 3 (1 agosto 1996): 492–94. http://dx.doi.org/10.1093/ml/77.3.492-b.

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Shalom, Stephen R. "Dropping Scores". Mathematics Magazine 68, n. 3 (giugno 1995): 220–23. http://dx.doi.org/10.1080/0025570x.1995.11996319.

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Weiland, Sue. "Music Scores". Technical Services Quarterly 10, n. 1 (8 febbraio 1993): 61–71. http://dx.doi.org/10.1300/j124v10n01_06.

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Majumdar, Boria. "Settling scores". Sport in Society 18, n. 3 (9 marzo 2015): 389–92. http://dx.doi.org/10.1080/17430437.2015.1010823.

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Rodger, M. "Quels scores ?" Journal des Maladies Vasculaires 38, n. 5 (ottobre 2013): 297. http://dx.doi.org/10.1016/j.jmv.2013.07.018.

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Zempsky, William T., John M. Corsi e Kathleen McKay. "Pain Scores". Pediatric Emergency Care 27, n. 1 (gennaio 2011): 27–28. http://dx.doi.org/10.1097/pec.0b013e318203ca03.

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Crookes, Jeanette. "Talking Scores". British Journal of Visual Impairment 14, n. 1 (gennaio 1996): 40–41. http://dx.doi.org/10.1177/026461969601400112.

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Martin, Gilbert I. "Apgar scores". American Journal of Obstetrics and Gynecology 178, n. 5 (maggio 1998): 1103. http://dx.doi.org/10.1016/s0002-9378(98)70565-4.

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Collins, David, Tamás Lengyel e Gregory P. Tollisen. "Keeping scores". Journal of Statistical Planning and Inference 137, n. 7 (luglio 2007): 2208–13. http://dx.doi.org/10.1016/j.jspi.2006.07.006.

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48

Luellen, Jason K., William R. Shadish e M. H. Clark. "Propensity Scores". Evaluation Review 29, n. 6 (dicembre 2005): 530–58. http://dx.doi.org/10.1177/0193841x05275596.

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49

Tonko, Sebastian, e Jean-François Dufour. "Hepatologische Scores". Therapeutische Umschau 70, n. 10 (1 ottobre 2013): 577–79. http://dx.doi.org/10.1024/0040-5930/a000450.

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Abstract (sommario):

Die folgenden hepatologischen Scores sind hilfreich dabei den Schweregrad für Lebererkrankungen einschätzen zu können, manche helfen bei der Diagnosestellung, andere können sogar dazu verwendet werden um eine Prognose für die weitere Entwicklung zu zeigen. Wir entschieden uns für 5 Scores welche im klinischen Alltag sehr nützlich sind: Child-Pugh-Turcotte-Score(CTP); MELD-Score, Score zur Diagnose einer Autoimmunhepatitis, den Mayo-Score für primär biliäre Zirrhose und den Lille-Score für alkoholische Hepatitis. In allen Scores werden klinische Merkmale und laborchemische Parameter verwendet um diese Aussagen treffen zu können.

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50

Sedgwick, P. "Prognostic scores". BMJ 348, jan17 1 (17 gennaio 2014): g282. http://dx.doi.org/10.1136/bmj.g282.

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