The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine
Abstract
The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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Cites
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“One strategy is to define the diagnostic problem in terms of measurable clinical outcomes (64).”
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Items 101–117 of 117
Tiribuzi, R., Orlacchio, A., Crispoltoni, L., Maiotti, M., Zampolini, M., De Angeliz, M., Mecocci, P., Cecchetti, R., Bernardi, G., Datti, A., et al. (2011) · Journal of Alzheimer’s Disease · doi:10.3233/jad-2011-100525 · Cited by 43
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Nonocclusive mesenteric infarction after cardiac surgery: potential biomarkers
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Validation study of Polar V800 accelerometer
Hernández-Vicente, A., Santos-Lozano, A., De Cocker, K., & Garatachea, N. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.07.16 · Cited by 23
Perceived Injustice Predicts Intention to Litigate: Findings from a Spinal Cord Injury Sample
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Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform
Yu, H., Li, F., Wu, T., Li, R., Yao, L., Wang, C., & Wu, X. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9816-6 · Cited by 17
The impact of quantitative optimization of hybridization conditions on gene expression analysis
Sykacek, P., Kreil, D. P., Meadows, L. A., Auburn, R. P., Fischer, B., Russell, S., & Micklem, G. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-73 · Cited by 13
Cicinelli, G., Nobile, E., Brighenti, S., Bari, S., Tonella, E., Aresi, A., Attanasio, M., Mazza, M., Valenti, M., & Keller, R. (2022) · Epidemiology and Psychiatric Sciences · doi:10.1017/s2045796022000506 · Cited by 12
“These differences are still relevant when considering cut points of ROC because 69 resulted to be the optimal cut point for women, 65 for men.”
MethodInfluentialDiagnostik psychischer Belastungen und Störungen bei körperlichen Erkrankungen
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Rogowska, A. M., Kwaśnicka, A., & Ochnik, D. (2021) · Psychology Research and Behavior Management · doi:10.2147/prbm.s308356 · Cited by 9
Ingram, M., Colloby, S. J., Firbank, M. J., Lloyd, J. J., O'Brien, J. T., & Taylor, J.-P. (2022) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2022.111460 · Cited by 9
Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model
University of Utah, Ewing, R., Sabouri, S., University of Utah, Park, K., University of Utah, Lyons, T., University of Utah, Tian, G., & University of Utah (2019) · doi:10.15760/trec.246 · Cited by 3
Clinical utility of urinary gonadotrophins in hypergonadotrophic states as Turner syndrome
Boncompagni, A., McNeilly, J., Murtaza, M., Lucaccioni, L., Iughetti, L., Wong, S. C., & Mason, A. (2020) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2020-0170 · Cited by 3
Metadata
- Authors
- Zweig, M. H., & Campbell, G.
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 1993-04-01
- Volume
- 39
- Issue
- 4
- Pages
- 561-577
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- OpenAlex
- W2104960492
- Cited by
- 6,569
- References
- 3
- FWCI
- 53.71
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
