The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Decision curve analysis to evaluate the clinical benefit of prediction models
Open accessAbstract
There is increased interest in the use of prediction models to guide clinical decision-making in orthopedics. Prediction models are typically evaluated in terms of their accuracy: discrimination (area-under-the-curve [AUC] or concordance index) and calibration (a plot of predicted vs. observed risk). But it can be hard to know how high an AUC has to be in order to be “high enough” to warrant use of a prediction model, or how much miscalibration would be disqualifying. Decision curve analysis was developed as a method to determine whether use of a prediction model in the clinic to inform decision-making would do more good than harm. Here we give a brief introduction to decision curve analysis, explaining the critical concepts of net benefit and threshold probability. We briefly review some prediction models reported in the orthopedic literature, demonstrating how use of decision curves has allowed conclusions as to the clinical value of a prediction model. Conversely, papers without decision curves were unable to address questions of clinical value. We recommend increased use of decision curve analysis to evaluate prediction models in the orthopedics literature.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
Self-citationMoons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Self-citationCalibration: the Achilles heel of predictive analytics
On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019) · BMC Medicine · doi:10.1186/s12916-019-1466-7 · Cited by 2,650
Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators
Van Calster, B., Wynants, L., Verbeek, J. F. M., Verbakel, J. Y., Christodoulou, E., Vickers, A. J., Roobol, M. J., & Steyerberg, E. W. (2018) · European Urology · doi:10.1016/j.eururo.2018.08.038 · Cited by 1,271
Self-citationA simple, step-by-step guide to interpreting decision curve analysis
Vickers, A. J., van Calster, B., & Steyerberg, E. W. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0064-7 · Cited by 1,174
Self-citationVickers, A. J., Van Calster, B., & Steyerberg, E. W. (2016) · BMJ · doi:10.1136/bmj.i6 · Cited by 1,163
Self-citationFitzgerald, M., Saville, B. R., & Lewis, R. J. (2015) · JAMA · doi:10.1001/jama.2015.37 · Cited by 787
Kerr, K. F., Brown, M. D., Zhu, K., & Janes, H. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.65.5654 · Cited by 697
Anemia and Patient Blood Management in Hip and Knee Surgery: A Systematic Review of the Literature
Spahn, D. R. (2010) · Anesthesiology · doi:10.1097/aln.0b013e3181e08e97 · Cited by 650
Fourney, D. R., Frangou, E. M., Ryken, T. C., DiPaola, C. P., Shaffrey, C. I., Berven, S. H., Bilsky, M. H., Harrop, J. S., Fehlings, M. G., Boriani, S., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2010.34.3897 · Cited by 551
Cited by
Wu, L., Mai, L., Wang, H., Huang, J., He, X., Zhan, X., Khalemsky, A., Kumar, V. A., Paxton, J. H., Tsilimingras, D., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02522-8 · Cited by 8
Metadata
- Authors
- Vickers, A. J., & Holland, F.
- Venue
- The Spine Journal
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 21
- Issue
- 10
- Pages
- 1643-1648
- ISSN
- 1529-9430 (ISSN-L)
- OpenAlex
- W3135278199
- Cited by
- 394
- References
- 21
- FWCI
- 34.39
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Decision curve analysis to evaluate the clinical benefit of prediction models. https://app.cochat.ai/papers/10.1016/j.spinee.2021.02.024
- The paper
- Vickers, A. J., & Holland, F. (2021). Decision curve analysis to evaluate the clinical benefit of prediction models. The Spine Journal. https://doi.org/10.1016/j.spinee.2021.02.024
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.