Hooft, Lotty

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 3,660

Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement

McInnes, M. D. F., Moher, D., Thombs, B. D., McGrath, T. A., Bossuyt, P. M., and the PRISMA-DTA Group, Clifford, T., Cohen, J. F., Deeks, J. J., Gatsonis, C., et al. (2018) · JAMA

Meta-analysis and systematic reviewsDelphi Technique in Research

Cited by 3,210

STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration

Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C. W., et al. (2016) · BMJ Open

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 2,570

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

STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · Clinical Chemistry

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 984

Prediction models for cardiovascular disease risk in the general population: systematic review

Damen, J. A. A. G., Hooft, L., Schuit, E., Debray, T. P. A., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C. M., Chiocchia, V., Roberts, C., et al. (2016) · BMJ

Health Promotion and Cardiovascular PreventionCardiovascular Health and Risk Factors

Cited by 935

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 852

Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist

Salameh, J.-P., Bossuyt, P. M., McGrath, T. A., Thombs, B. D., Hyde, C. J., Macaskill, P., Deeks, J. J., Leeflang, M., Korevaar, D. A., Whiting, P., et al. (2020) · BMJ

Meta-analysis and systematic reviewsClinical practice guidelines implementation

Cited by 719

A guide to systematic review and meta-analysis of prediction model performance

Debray, T. P. A., Damen, J. A. A. G., Snell, K. I. E., Ensor, J., Hooft, L., Reitsma, J. B., Riley, R. D., & Moons, K. G. M. (2017) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 657

Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection

Dinnes, J., Deeks, J. J., Berhane, S., Taylor, M., Adriano, A., Davenport, C., Dittrich, S., Emperador, D., Takwoingi, Y., Cunningham, J., et al. (2021) · Cochrane Database of Systematic Reviews

SARS-CoV-2 detection and testingCOVID-19 Clinical Research Studies

Cited by 591

Variation of a test’s sensitivity and specificity with disease prevalence

Leeflang, M. M. G., Rutjes, A. W. S., Reitsma, J. B., Hooft, L., & Bossuyt, P. M. M. (2013) · Canadian Medical Association Journal

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 530

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

de Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 499

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 415

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 352

Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol

Sounderajah, V., Ashrafian, H., Golub, R. M., Shetty, S., De Fauw, J., Hooft, L., Moons, K., Collins, G., Moher, D., Bossuyt, P. M., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 333

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

Debray, T. P., Damen, J. A., Riley, R. D., Snell, K., Reitsma, J. B., Hooft, L., Collins, G. S., & Moons, K. G. (2019) · Statistical Methods in Medical Research

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 278

Continuous glucose monitoring systems for type 1 diabetes mellitus

Langendam, M., Luijf, Y. M., Hooft, L., DeVries, J. H., Mudde, A. H., & Scholten, R. J. (2012) · Cochrane Database of Systematic Reviews

Diabetes Management and ResearchDiabetes Management and Education

Cited by 262

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis

Damen, J. A., Pajouheshnia, R., Heus, P., Moons, K. G. M., Reitsma, J. B., Scholten, R. J. P. M., Hooft, L., & Debray, T. P. A. (2019) · BMC Medicine

Health Promotion and Cardiovascular PreventionLipoproteins and Cardiovascular Health

Cited by 250

Barriers to GPs' use of evidence-based medicine: a systematic review

Zwolsman, S., te Pas, E., Hooft, L., Waard, M. W.-D., & van Dijk, N. (2012) · British Journal of General Practice

Health Sciences Research and EducationClinical practice guidelines implementation

Cited by 208

Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies

Hooijmans, C. R., de Vries, R. B. M., Ritskes-Hoitinga, M., Rovers, M. M., Leeflang, M. M., IntHout, J., Wever, K. E., Hooft, L., de Beer, H., Kuijpers, T., et al. (2018) · PLOS ONE

Meta-analysis and systematic reviewsAnimal testing and alternatives

Cited by 176

Overinterpretation and Misreporting of Diagnostic Accuracy Studies: Evidence of “Spin”

Ochodo, E. A., de Haan, M. C., Reitsma, J. B., Hooft, L., Bossuyt, P. M., & Leeflang, M. M. G. (2013) · Radiology

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 158

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · BMC Medical Research Methodology

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 140

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Andaur Navarro, C. L., Damen, J. A. A., van Smeden, M., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., et al. (2023) · Journal of Clinical Epidemiology

Artificial Intelligence in HealthcareStatistical Methods in Epidemiology

Cited by 121

Reporting quality of diagnostic accuracy studies: a systematic review and meta-analysis of investigations on adherence to STARD

Korevaar, D. A., van Enst, W. A., Spijker, R., Bossuyt, P. M. M., & Hooft, L. (2014) · Evidence Based Medicine

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 119