Cited by 371Statistical Methods in EpidemiologyClinical Reasoning and Diagnostic SkillsMeta-analysis and systematic reviews

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

Calibration of Risk Prediction Models: Impact on Decision-Analytic Performance

Open access

Van Calster, Ben, Vickers, Andrew J.·2015·Medical Decision Making

DOI 10.1177/0272989x14547233

Open this paper in CoChat — free account Open PDF

Abstract

Decision-analytic measures to assess clinical utility of prediction models and diagnostic tests incorporate the relative clinical consequences of true and false positives without the need for external information such as monetary costs. Net Benefit is a commonly used metric that weights the relative consequences in terms of the risk threshold at which a patient would opt for treatment. Theoretical results demonstrate that clinical utility is affected by a model';s calibration, the extent to which estimated risks correspond to observed event rates. We analyzed the effects of different types of miscalibration on Net Benefit and investigated whether and under what circumstances miscalibration can make a model clinically harmful. Clinical harm is defined as a lower Net Benefit compared with classifying all patients as positive or negative by default. We used simulated data to investigate the effect of overestimation, underestimation, overfitting (estimated risks too extreme), and underfitting (estimated risks too close to baseline risk) on Net Benefit for different choices of the risk threshold. In accordance with theory, we observed that miscalibration always reduced Net Benefit. Harm was sometimes observed when models underestimated risk at a threshold below the event rate (as in underestimation and overfitting) or overestimated risk at a threshold above event rate (as in overestimation and overfitting). Underfitting never resulted in a harmful model. The impact of miscalibration decreased with increasing discrimination. Net Benefit was less sensitive to miscalibration for risk thresholds close to the event rate than for other thresholds. We illustrate these findings with examples from the literature and with a case study on testicular cancer diagnosis. Our findings strengthen the importance of obtaining calibrated risk models.

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

Crossref · 10.1177/0272989x14547233

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

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.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
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
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers
Pencina, M. J., D’Agostino, R. B., & Steyerberg, E. W. (2010) · Statistics in Medicine · doi:10.1002/sim.4085 · Cited by 2,483
Clinical Prediction Models
Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491
Criteria for Evaluation of Novel Markers of Cardiovascular Risk
Hlatky, M. A., Greenland, P., Arnett, D. K., Ballantyne, C. M., Criqui, M. H., Elkind, M. S. V., Go, A. S., Harrell, F. E., Hong, Y., Howard, B. V., et al. (2009) · Circulation · doi:10.1161/circulationaha.109.192278 · Cited by 1,146
Reporting and Methods in Clinical Prediction Research: A Systematic Review
Bouwmeester, W., Zuithoff, N. P. A., Mallett, S., Geerlings, M. I., Vergouwe, Y., Steyerberg, E. W., Altman, D. G., & Moons, K. G. M. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001221 · Cited by 600
Two further applications of a model for binary regression
COX, D. R. (1958) · Biometrika · doi:10.1093/biomet/45.3-4.562 · Cited by 556
Therapeutic decision making: a cost-benefit analysis
Pauker, S. G., & Kassirer, J. P. (1975) · New England Journal of Medicine · doi:10.1056/nejm197507312930505 · Cited by 507
Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers
Austin, P. C., & Steyerberg, E. W. (2014) · Statistics in Medicine · doi:10.1002/sim.5941 · Cited by 362

Show all 29 references

Research works printed but not linked: 1

Vickers AJ, 2010, Semin Oncol, 76, 1298

Sorted by citations

Items 301–371 of 371

SPUR: A Patient-Reported Medication Adherence Model as a Predictor of Admission and Early Readmission in Patients Living with Type 2 Diabetes
Wells, J. S., Wang, C., Dolgin, K., & Kayyali, R. (2023) · Patient Preference and Adherence · doi:10.2147/ppa.s397424 · Cited by 1
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Translation into Russian
Moons, K. G. M., Altman, D. G., Reitsma, J. B., loannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, P. G. S. (2023) · Вопросы современной педиатрии · doi:10.15690/vsp.v22i2.2557 · Cited by 1
Development and Validation of the CANHEART Population-Based Laboratory Prediction Models for Atherosclerotic Cardiovascular Disease
Sud, M. K., Sivaswamy, A., Austin, P. C., Anderson, T. J., Naimark, D. M. J., Farkouh, M. E., Lee, D. S., Roifman, I., Thanassoulis, G., Tu, K., et al. (2023) · Annals of Internal Medicine · doi:10.7326/m23-1345 · Cited by 2
Development and validation of a dynamic nomogram for short-term survival in acute heart failure patients with acute kidney injury upon ICU admission
Liao, T., Su, T., Yang, L., Huang, L., & Feng, L.-H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39494 · Cited by 1
Development and validation of a dynamic nomogram for acute kidney injury prediction in ICU patients with acute heart failure
Feng, L.-H., Su, T., Huang, L., Liao, T., Yang, L., & Wu, L. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1544024 · Cited by 2
Validation of the prostate health index in a predictive model of prostate cancer
Bonet, A., Barrionuevo-González, M., Bajo, A. M., Pulido-Fonseca, L., Polledo, L. E. O., Ruíz, J. C. T., & Sánchez‐Chapado, M. (2017) · Actas Urológicas Españolas (English Edition) · doi:10.1016/j.acuroe.2017.11.006 · Cited by 1
Trust and Recognition
Stahl, J. E. (2014) · Medical Decision Making · doi:10.1177/0272989x14563080 · Cited by 1
Real-world usage diminishes validity of Artificial Intelligence toolsPreprint
Vaid, A., Sawant, A. S., Suárez‐Fariñas, M., Lee, J., Kaul, S. K., Kovatch, P., Freeman, R., Jiang, J. J., Jayaraman, P., Fayad, Z. A., et al. (2022) · medRxiv · doi:10.1101/2022.11.17.22282440 · Cited by 1
Commentary on “A systematic review on machine learning and deep learning techniques in cancer survival prediction”: Validation of survival methods
Sidorova, J., & Lozano, J. J. (2023) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2023.08.001 · Cited by 1
The Dilemma of Incomplete Reperfusion After Thrombectomy for Ischemic Stroke
Dippel, D. W. J., & Klaveren, D. V. (2024) · Neurology · doi:10.1212/wnl.0000000000209646 · Cited by 1
Electronic Health Record-Based Prediction Models to Inform Decisions about HIV Pre-exposure Prophylaxis: A Systematic ReviewPreprint
Agovi, A. M., Thompson, C. T., Meadows, R. J., Lu, Y., & Ojha, R. P. (2025) · medRxiv · doi:10.1101/2025.01.17.25320732 · Cited by 1
Biological aging markers and polygenic risk scores for mortality prediction: a multicohort studyPreprint
Mostafaei, S., Lindh, J., Qin, C., Mak, J. K. L., & Hägg, S. (2026) · Research Square · doi:10.21203/rs.3.rs-9600666/v1 · Cited by 1
Reply to Akinosoglou and Gogos: Sepsis-3 in Community-acquired Pneumonia: How Reliable Is It?
Ranzani, O., & Torres, A. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201708-1598le
“It is important to note that even with certain mis-calibration (6), qSOFA outperformed SIRS in all scenarios for risk-stratification in our study, including the decision-curve analysis.”
Method
Risk Projection for Time-to-Event Outcome Leveraging Summary Statistics With Source Individual-Level DataPreprint
Zheng, J., Zheng, Y., & Hsu, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01850
“Therefore, model recalibration is critically needed for accurate risk projection in the target population, but it is frequently under-appreciated (Van Calster and Vickers 2015).”
Background
The Generalizability of Clinical Prediction Models for Patients with Acute Coronary Syndromes: Results from Independent External ValidationsPreprint
Wessler, B. S., Nelson, J., Park, J. G., McGinnes, H. L., Upshaw, J. N., CALSTER, B. V., Klaveren, D. V., Steyerberg, E. W., & Kent, D. M. (2021) · medRxiv · doi:10.1101/2021.01.21.21250234
Stabilizing Calibration of Clinical Prediction Models in Non-Stationary Environments: Methods Supporting Data-Driven Model Updating
Davis, S. E. (2019)
Promotion, Integration, Management and Processing of Critical Inpatients’ Open Big Data Repositories
de la Hoz, M. Á. A. (2020) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.63196
Peer Review #2 of "A model of multiple tumor marker for lymph node metastasis assessment in colorectal cancer: a retrospective study (v0.1)"
(2022) · PeerJ · doi:10.7287/peerj.13196v0.1/reviews/2
Peer Review #2 of "A model of multiple tumor marker for lymph node metastasis assessment in colorectal cancer: a retrospective study (v0.2)"
(2022) · PeerJ · doi:10.7287/peerj.13196v0.2/reviews/2
Peer Review #1 of "A model of multiple tumor marker for lymph node metastasis assessment in colorectal cancer: a retrospective study (v0.1)"
(2022) · PeerJ · doi:10.7287/peerj.13196v0.1/reviews/1
Development and validation of a model for predicting the risk of brain arteriovenous malformation rupture based on three-dimensional morphological features
Zhang, S., Sun, S., Zhai, Y., Wang, X., Zhang, Q., Shi, Z., Ge, P., & Zhang, D. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.979014
Improving the surgical care of prostate cancer patients through reflection on clinical data
Veerman, A. N. (2023) · doi:10.5463/thesis.206
Development and External Validation of Partial Proportional Odds Risk Prediction Models for Cancer Stage at Diagnosis among Males and Females in Canada
Biziaev, T., Aktary, M. L., Wang, Q., Chekouo, T., Bhatti, P., Shack, L., Robson, P. J., & Kopciuk, K. A. (2023) · Cancers · doi:10.3390/cancers15143545 · Cited by 1
A Predictive Model to Estimate Immunogenicity after Receipt of SARS-CoV-2 Vaccines in People with Gastrointestinal Cancer
王, 靖. (2023) · Advances in Clinical Medicine · doi:10.12677/acm.2023.1392108
Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the National Quality Improvement Program Database Using Machine Learning
Yeramosu, T., Krivicich, L. M., Puzzitiello, R. N., Guenthner, G., & Salzler, M. J. (2024) · Arthroscopy The Journal of Arthroscopic and Related Surgery · doi:10.1016/j.arthro.2024.07.019
Evaluating the performance of the SORG machine learning algorithm for predicting discharge disposition in lumbar surgery patients
Salim, O., Draz, M. S., Bligh, E. R., & Mathieson, C. S. (2024) · Seminars in Spine Surgery · doi:10.1016/j.semss.2024.101132 · Cited by 1
Vorhersage von Psoriasis‐Arthritis bei Patienten mit Plaque‐Psoriasis durch neuartiges Nomogramm
Tan, M., Chen, J., Cheng, J., Hu, J., Hu, K., Yang, J. X., Li, X., Zhang, M., Zhu, W., Liao, L., et al. (2024) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.15446_g
Development and validation of a user-friendly prediction tool for preoperative T-Staging in gallbladder Cancer: A multicenter study using contrast-enhanced CT-Based fusion models
He, X., Long, Y., Huang, J., Liao, J., & Zhang, L. (2025) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2025.110117
FIR-LSTM: An Explainable Deep Learning Framework for Predicting Iatrogenic Withdrawal Syndrome in Pediatric Intensive Care UnitsPreprint
Zhang, L., Song, H., Patel, A., Pollack, M. M., & Watson, L. T. (2025) · Research Square · doi:10.21203/rs.3.rs-6787167/v1
“In the clinical setting, this calibration enables reliable risk stratification, supporting decisions such as anticipatory monitoring, medication tapering, or early intervention 27,28 , all of which require nuanced risk awareness rather than binary prediction alone.”
Validation of Caprini risk model and development of a biomarker-driven predictive model for postoperative venous thromboembolism in gynecologic cancer surgeryPreprint
Lin, Y., Yang, L., Chen, H., Zhang, L., Chen, J., Zheng, Y., Tang, F., & Lin, J. (2025) · Research Square · doi:10.21203/rs.3.rs-7079505/v1
Preoperative Prediction of Neck Lymph Node Metastasis in Oral Squamous Cell Carcinoma Using 18 F-FDG PET/CT-based Radiomics AnalysisPreprint
Huang, C., Chen, Z., Chen, S., Wu, Z., Zhao, X., & Miao, W. (2025) · Research Square · doi:10.21203/rs.3.rs-7389512/v1
Development and Validation of an Extra Spindle Pole Bodies–like 1–Based Diagnostic and Prognostic Model for Hepatitis B Virus–Related Hepatocellular Carcinoma: Retrospective Cohort Study
Feng, L.-H., Liang, H., Hu, B., Wei, L., Li, Q., Su, T., Yin, Q., Feng, Y., Huang, L., Liang, H., et al. (2025) · JMIR Medical Informatics · doi:10.2196/78354
Systematic evaluation of predictive models for futile recanalization before thrombectomy in patients with acute ischemic stroke
Chen, C., Liu, L., Liu, X., & Ya, T. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1625236
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
Additional file 5 of Validation and update of a multivariable prediction model for the identification and management of patients at risk for hepatocellular carcinoma
Li, B., Zhao, Y., 蔡望喜, 明安萍, & Li, H. (2021) · Figshare · doi:10.6084/m9.figshare.15567433
Additional file 1 of Prognostic models for COVID-19 needed updating to warrant transportability over time and space
van Klaveren, D., Zanos, T. P., Nelson, J., Levy, T. J., Park, J. G., Retel Helmrich, I. R. A., Rietjens, J. A. C., Basile, M. J., Hajizadeh, N., Lingsma, H. F., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21622824
Additional file 1 of Validation and update of a multivariable prediction model for the identification and management of patients at risk for hepatocellular carcinoma
Li, B., Zhao, Y., 蔡望喜, 明安萍, & Li, H. (2021) · Figshare · doi:10.6084/m9.figshare.15567343
Additional file 1 of Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization
Zhong, X., Li, L., Jiang, H., Yin, J., Lu, B., Han, W. G., Li, J., & Zhang, J. (2020) · Figshare · doi:10.6084/m9.figshare.12904778
Diagnostic value of serological scores for the detection of liver steatosis in people with HIV in low- and middle-income countries
Plaisy, M. K., Riebensahm, C., Moreira, R., Samala, N., Borse, R. T., Kuniholm, M. H., Minga, A. K., Wandeler, G., López-Íñiguez, Á., Michael, D., et al. (2025) · AIDS · doi:10.1097/qad.0000000000004434
CatBoost Machine Learning Model for Thrombosis Risk Prediction in Critically Ill Cancer Patients: A MIMIC-IV Database Study
Yang, C., Ma, H., Zeng, X., Yang, J., Xiao, B., Tan, R., Liu, Y., & Zeng, Q. (2026) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296251408357
Prediction models for primary graft dysfunction after lung transplantation: Systematic review
Zhao, R., Bu, X., & fan, R. (2026) · Transplantation Reviews · doi:10.1016/j.trre.2026.101017
Individualized survival prediction in medullary thyroid carcinoma using a least absolute shrinkage and selection operator-Cox-based nomogram
Zhan, Z., Cao, W., Huang, S., Gui, X., & Li, J. (2026) · Journal of International Medical Research · doi:10.1177/03000605261431643
Multi-omics gut microbiome signatures for treat-to-target management in inflammatory bowel disease
Cai, X. T., Yao, Y., Zheng, Y., & Zhao, X. (2026) · Microbiological Research · doi:10.1016/j.micres.2026.128511
Méthodologie de l’évaluation des biomarqueurs prédictifs quantitatifs et de la détermination d’un seuil pour leur utilisation en médecine personnalisée
Blangero, Y. (2019) · doi:10.70675/fc4aef03z074cz4106zb36ezdc4522e93a2f
Short-term risk stratification using parallel admission and reassessment features in PICU patients with infection
Wang, Y., Chen, C., Jiang, G., & Xuan, C. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1834603
Subgroup deployment-readiness audit of calibration and clinical utility in perioperative infection prediction
Guillen-Ramirez, H., Lucas, K. L., Wintsch, Y. M., Blatter, T. U., Triep, K., Endrich, O., & Beldi, G. (2026) · Journal of the American Medical Informatics Association · doi:10.64898/2026.06.15.26355656
Development and internal validation of a risk prediction model for gastric precancerous lesions integrating Helicobacter pylori serologic typing and host susceptibility polymorphisms
Yu, H., Li, P., Gong, S., Zhao, J., & Jiang, K. (2026) · Medicine · doi:10.1097/md.0000000000049606
Interpretable early mortality prediction in oncology ICU patients: A dual-cohort validation of a LASSO–XGBoost–SHAP framework
Chen, X., Wang, L., Qin, W., Yang, M., Yan, Y., Luo, X. P., & Yuan, X. (2026) · Digital Health · doi:10.1177/20552076261466804
Clinical target volume radiomics from planning CT for pretreatment response prediction in rectal cancer undergoing chemoradiotherapy
Huang, Y.-C., Huang, S.-M., Yeh, J.-H., & Tu, S.-J. (2026) · Strahlentherapie und Onkologie (Print) · doi:10.1007/s00066-026-02569-2
A prealbumin-containing TyG–FPR–AISI composite for clinically significant early sepsis-associated acute kidney injury: a single-center derivation cohort study with internal validation
Gao, Y., Liu, C. P., Xiong, X., Yang, C., Tang, S., Wan, J., Chen, Q., Jin, L., Wang, L. Q., Xia, H., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1825792
Incomplete reporting persists in orthopedic machine learning models: a systematic review of TRIPOD and TRIPOD+AI reporting completeness
Kuijten, R. H., Groot, T. M., van Weezenbeek, M. A., Doornberg, J. N., Verlaan, J., & Groot, O. Q. (2026) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2026.112438
Reply to ‘Interpreting the clinical utility and generalizability of a multitask perioperative prediction model’
Yoon, H., Lee, H., & Lee, H. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02995-7
Beyond single cut-off values in ultrasound: limitations and implications for context-aware radiology reporting
Schöpper, I., Dohna, M., & Bansmann, P. M. (2026) · Insights into Imaging · doi:10.1186/s13244-026-02371-9
Machine learning-based prediction model for postpartum stress urinary incontinence risk: a systematic review and meta-analysis
Zhong, X., Wang, Y. F., Chai, W., Lin, Y., Zheng, N., & Huang, J. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1875678
From Cardiovascular Risk Prediction to Precision Prevention: Methodological, Contextual, and Implementation Challenges
Sposito, A. C. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.050232
Early Radiographic Changes Predict Radiographic Stability in Distal Radius Fractures: Development and Validation of an Explainable Predictive Model
Yeramosu, T., Khorram, R., Phal, A., Beredjiklian, P. K., Eygendaal, D. E., Colaris, J. W., Doornberg, J. N., & Kachooei, A. R. (2026) · Journal of Hand Surgery Global Online · doi:10.1016/j.jhsg.2026.101118
Development and preliminary validation of a screening model for metabolic syndrome using routine indicators from the CHARLS cohortPreprint
Liu, W., Li, S., Zeng, Y., Dai, W., Peng, J., Wang, X., Chen, Q., Li, W., Du, D., Lu, S., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10772120/v1
Association of the GLM7 composite metabolic index with gestational diabetes mellitus in early pregnancy: a secondary analysis of a Korean prospective pregnancy cohort
Zhao, Q., Hu, W., Xi, X., Wu, L., Hu, S., Liu, Y., & Chen, Y. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1907985
Toward Clinically Trustworthy Pathology Foundation Models for Microsatellite Instability Prescreening in Colorectal Cancer
Nguyen, N. H., Lý, K. N., & Le, N. Q. K. (2026) · Computers · doi:10.3390/computers15090626
A Similarity-Weighted Ensemble Learning Framework for Cross-Regional Maternal Risk Stratification in Tanzania: A Methodological Demonstration with Interpretability and Fairness Auditing
Patil, D. D., & Kamlakar, M. (2026) · International Conference on Modern Sustainable Systems (CMSS) · doi:10.1109/cmss69636.2026.11689059
Multimodal prediction of MCI-to-dementia conversion over a two-year window
Simkhada, B., Liang, T. Y., Cui, X., Adeyosoye, M., Simkhada, B., Cabrerizo, M., Cid, R. C., Burke, S. L., Barreto, A., Rishe, N., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02551-x
Calibration Shrinkage and Microvascular Quantification in a Renal Fibrosis Nomogram
Shao, J., & Gu, J. (2026) · Journal of Ultrasound in Medicine · doi:10.1002/jum.70492
Development of risk prediction model for delirium using routinely collected electronic health records
Gokhale, S., Belsti, Y., Taylor, D., Zeps, N., Nagpurkar, M., Teede, H., & Enticott, J. (2026) · Archives of Gerontology and Geriatrics Plus · doi:10.1016/j.aggp.2026.100338
Predicting Inflammatory Adaptation to Exercise via Circulating miR-146a and Machine Learning
Mourgan, F. H. A. E. (2026) · Biomedical and Biotechnology Research Journal (BBRJ) · doi:10.4103/bbrj.bbrj_83_26
What would universal screening for cerebral palsy be worth? The case for net benefit analysis
Briganti, G., & McNally, R. J. (2026) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.70553
Predictive performance of machine learning models for bloodstream infection by validation type: A systematic review and meta-analysis
Kwon, H., Kim, J., Yun, H., & Park, S. Y. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106740
From discovery to proof: a Translation Evidence Map for precision psychiatry
Baune, B. T. (2026) · The Lancet Psychiatry · doi:10.1016/s2215-0366(26)00267-1
Why decision curves cross treat-all and treat-none: A PPV and calibration perspective
Hoessly, L. (2026) · Global Epidemiology · doi:10.1016/j.gloepi.2026.100290
Development and internal validation of prediction models for ICU-level intervention and 14-day mortality at Medical Emergency Team activation: A retrospective cohort study
島内, 淳., Nishino, T., Shigeta, K., Mase, H., Yamamoto, T., Yokobori, S., & Ishikawa, M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0360511
TyG-ABSI and TyG-WWI as insulin-resistance body shape markers of mortality in middle-aged and older adults within the cardiovascular-kidney-metabolic framework
Xiao, F., Guo, H., Wang, L., Qu, Y., & Dai, C. (2026) · Medicine · doi:10.1097/md.0000000000051089
Concurrent Diabetes Mellitus and Periodontitis in U.S. Adults: Mortality Associations and Machine Learning Risk Prediction
Li, J., Zhu, T., & Kashiwazaki, H. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15208138

Authors: Van Calster, B., & Vickers, A. J.
Affiliations: KU Leuven, Department of Development and Regeneration, Leuven, Belgium (BVC)
Memorial Sloan Kettering Cancer Center (US)
ORCID: Vickers, Andrew J.
Venue: Medical Decision Making
Publisher: SAGE Publications
Published: 2014-08-25
Volume: 35
Issue: 2
Pages: 162-169
ISSN: 0272-989X (ISSN-L)
1552-681X
DOI: 10.1177/0272989x14547233
OpenAlex: W2054193584
Cited by: 371
References: 29
Citations by year: 2026: 44 · 2025: 39 · 2024: 48 · 2023: 40 · 2022: 57 · 2021: 40 · 2020: 31 · 2019: 29 · 2018: 10 · 2017: 19 · 2016: 7 · 2015: 6 · 2014: 1
FWCI: 11.61
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Medical Decision Making (publishedVersion)
PubMed (publishedVersion)
Lirias (submittedVersion, other-oa)
RePEc: Research Papers in Economics (submittedVersion)
Funders: Fonds Wetenschappelijk Onderzoek
Vlaamse regering
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: risk prediction model, model calibration, net benefit, clinical utility, calibration error, overfitting, underfitting, risk overestimation, risk underestimation, risk thresholds, model discrimination, testicular cancer diagnosis
MeSH terms: Computer Simulation, Decision Support Techniques, Humans, Male, Models, Theoretical, Norway, Predictive Value of Tests, ROC Curve, Testicular Neoplasms, Bias, Linear Models, Risk Assessment

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

Calibration of Risk Prediction Models: Impact on Decision-Analytic Performance

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks