Wynants, Laure

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · BMJ

COVID-19 Clinical Research StudiesStatistical Methods in Epidemiology

Cited by 3,070

Calibration: 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

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 2,650

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

Posttraumatic stress, anxiety and depression following miscarriage and ectopic pregnancy: a multicenter, prospective, cohort study

Farren, J., Jalmbrant, M., Falconieri, N., Mitchell-Jones, N., Bobdiwala, S., Al-Memar, M., Tapp, S., Van Calster, B., Wynants, L., Timmerman, D., et al. (2020) · American Journal of Obstetrics and Gynecology

Maternal Mental Health During Pregnancy and PostpartumGrief, Bereavement, and Mental Health

Cited by 299

There is no such thing as a validated prediction model

Van Calster, B., Steyerberg, E. W., Wynants, L., & van Smeden, M. (2023) · BMC Medicine

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 286

Predictive analytics in health care: how can we know it works?

Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 223

Three myths about risk thresholds for prediction models

on behalf of the Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Wynants, L., van Smeden, M., McLernon, D. J., Timmerman, D., Steyerberg, E. W., & Van Calster, B. (2019) · BMC Medicine

Statistical Methods in EpidemiologyClinical Reasoning and Diagnostic Skills

Cited by 213

ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models

Verbakel, J. Y., Steyerberg, E. W., Uno, H., De Cock, B., Wynants, L., Collins, G. S., & Van Calster, B. (2020) · Journal of Clinical Epidemiology

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 138

Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationStatistical Methods in Epidemiology

Cited by 112

Methodology over metrics: current scientific standards are a disservice to patients and society

Van Calster, B., Wynants, L., Riley, R. D., van Smeden, M., & Collins, G. S. (2021) · Journal of Clinical Epidemiology

Meta-analysis and systematic reviewsAcademic Publishing and Open Access

Cited by 105

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies

Barreñada, L., Ledger, A., Dhiman, P., Collins, G., Wynants, L., Verbakel, J. Y., Timmerman, D., Valentin, L., & Van Calster, B. (2024) · BMJ Medicine

Ovarian cancer diagnosis and treatmentBRCA gene mutations in cancer

Cited by 42

Ultrasound‐based risk model for preoperative prediction of lymph‐node metastases in women with endometrial cancer: model‐development study

Eriksson, L. S. E., Epstein, E., Testa, A. C., Fischerova, D., Valentin, L., Sladkevicius, P., Franchi, D., Frühauf, F., Fruscio, R., Haak, L. A., et al. (2020) · Ultrasound in Obstetrics & Gynecology

Endometrial and Cervical Cancer TreatmentsOvarian cancer diagnosis and treatment

Cited by 24

Assessing the quality of prediction models in health care using the Prediction model Risk Of Bias ASsessment Tool (PROBAST): an evaluation of its use and practical application

Kaul, T., Damen, J. A. A., Wynants, L., Van Calster, B., van Smeden, M., Hooft, L., & Moons, K. G. M. (2025) · Journal of Clinical Epidemiology

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 16

Adherence rates to a prediction tool identifying women with an increased gestational diabetes risk: An implementation study

van Montfort, P., Scheepers, H. C. J., van Dooren, I. M. A., Meertens, L. J. E., Wynants, L., Zelis, M., Zwaan, I. M., Spaanderman, M. E. A., & Smits, L. J. M. (2021) · International Journal of Gynecology & Obstetrics

Gestational Diabetes Research and ManagementMaternal and Perinatal Health Interventions

Cited by 4