The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
A guide to systematic review and meta-analysis of prediction model performance
Open accessAbstract
Validation of prediction models is highly recommended and increasingly common in the literature. A systematic review of validation studies is therefore helpful, with meta-analysis needed to summarise the predictive performance of the model being validated across different settings and populations. This article provides guidance for researchers systematically reviewing and meta-analysing the existing evidence on a specific prediction model, discusses good practice when quantitatively summarising the predictive performance of the model across studies, and provides recommendations for interpreting meta-analysis estimates of model performance. We present key steps of the meta-analysis and illustrate each step in an example review, by summarising the discrimination and calibration performance of the EuroSCORE for predicting operative mortality in patients undergoing coronary artery bypass grafting.
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.
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?
In the citing paper
“These methods mainly focused on (diagnostic) prediction models with a binary outcome, and may therefore have limited value when reviewing (prognostic) models with a time-to-event outcome.”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
In the citing paper
“In particular, the total O:E ratio quantifies the averaged observed:expected ratio across the entire range of predicted risks, and is strongly related to calibration-in-the-large.14”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
In the citing paper
“In particular, discrimination and calibration performance are highly dependent on patient spectrum (case-mix variation) and therefore most likely to vary across validation studies.14”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
In the citing paper
“However, it is common for validation studies to present information on the total number of observed (O) and expected (E) events, or the corresponding observed frequencies PO and PE. These quantities can then be used to calculate the total O:E ratio”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
In the citing paper
“Quantitative synthesis of prediction model studies may help to better understand their potential generalizability and can be achieved by applying meta-analysis methods.14”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [14]
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
Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal
Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648
Estimating the mean and variance from the median, range, and the size of a sample
Hozo, S. P., Djulbegovic, B., & Hozo, I. (2005) · BMC Medical Research Methodology · doi:10.1186/1471-2288-5-13 · Cited by 9,062
Moons, 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-citationCollins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267
Self-citationInterpretation of random effects meta-analyses
Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011) · BMJ · doi:10.1136/bmj.d549 · Cited by 2,884
Self-citationChapter 9: Analysing Data and Undertaking Meta-Analyses
Deeks, J. J., Higgins, J. P. T., & Altman, D. G. (2008) · Cited by 2,782
IntHout, J., Ioannidis, J. P., & Borm, G. F. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-25 · Cited by 2,419
Stewart, L. A., Clarke, M., Rovers, M., Riley, R. D., Simmonds, M., Stewart, G., & Tierney, J. F. (2015) · JAMA · doi:10.1001/jama.2015.3656 · Cited by 2,134
Self-citationMoons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 648
Radiomics in Carotid Plaque: A Systematic Review and Radiomics Quality Score Assessment
Hou, C., Liu, X., Du, Y., Cheng, L., Liu, L., Nie, F., Zhang, W., & He, W. (2023) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2023.06.008 · Cited by 19
A Systematic Review of the Single-Item Burnout Question: Its Reliability Depends on Your Purpose
Hagan, G., Okut, H., & Badgett, R. G. (2024) · Journal of General Internal Medicine · doi:10.1007/s11606-024-08685-y · Cited by 18
Stroke risk prediction models: A systematic review and meta-analysis
Asowata, O. J., Okekunle, A. P., Olaiya, M. T., Akinyemi, J. O., Owolabi, M. O., & Akpa, O. M. (2024) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2024.122997 · Cited by 18
Systematic Reviews and Meta-analysis: Principles and Practice
Shrestha, B. M. (2019) · Journal of Nepal Medical Association · doi:10.31729/jnma.3986 · Cited by 17
Yang, L., Wang, Q., Cui, T., Huang, J., & Jin, H. (2021) · Canadian Journal of Gastroenterology and Hepatology · doi:10.1155/2021/9996358 · Cited by 17
Tian, Y., Shang, Y., Tong, D., Chi, S., Li, J., Kong, X., Ding, K., & Li, J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.008 · Cited by 18
Ensor, J., Snell, K. I. E., Debray, T. P. A., Lambert, P. C., Look, M. P., Mamas, M. A., Moons, K. G. M., & Riley, R. D. (2021) · Statistics in Medicine · doi:10.1002/sim.8959 · Cited by 17
Self-citationDamen, J. A., & Hooft, L. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0049-6 · Cited by 17
Self-citationRisk prediction models for disability in older adults: a systematic review and critical appraisal
Zhou, J., Xu, Y., Yang, D., Zhou, Q., Ding, S., & Pan, H. (2024) · BMC Geriatrics · doi:10.1186/s12877-024-05409-z · Cited by 17
Chen, H., Han, J. H., Li, J., Xiong, J., Wang, D., Han, M., Shen, Y., & Lu, W. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1522911 · Cited by 16
Zhou, Z., Zhang, J., Fan, C., Wei, Z., Wang, Q., & Liu, C. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1662409 · Cited by 16
Ji, Z., Li, X., Lei, S., Xu, J., & Xie, Y. (2023) · The Clinical Respiratory Journal · doi:10.1111/crj.13606 · Cited by 16
Marzano, L. A., Kazory, A., Husain‐Syed, F., & Ronco, C. (2023) · Clinical Endocrinology · doi:10.1111/cen.14918 · Cited by 16
Furukawa, T. A., Kato, T., Shinagawa, Y., Miki, K., Fujita, H., Tsujino, N., Kondo, M., Inagaki, M., & Yamada, M. (2018) · Psychological Medicine · doi:10.1017/s0033291718003331 · Cited by 16
Sauerbrei, W., Bland, M., Evans, S. J. W., Riley, R. D., Royston, P. J., Schumacher, M. C., & Collins, P. G. S. (2020) · Biometrical Journal · doi:10.1002/bimj.202000053 · Cited by 17
Self-citationLi, L., Li, X., Li, W., Ding, X., Zhang, Y., Chen, J., & Li, W. (2022) · BMC Cancer · doi:10.1186/s12885-022-09841-5 · Cited by 16
Sacco, E., Campetella, M., Marino, F., Gavi, F., Moretto, S., Pastorino, R., Bizzarri, F. P., Pierconti, F., Gandi, C., & Bientinesi, R. (2025) · European Urology Open Science · doi:10.1016/j.euros.2025.05.012 · Cited by 15
Predictive Models for Estimating the Probability of Successful Vaginal Birth After Cesarean Delivery
Black, N., Henderson, I., Wattar, B. H. A., & Quenby, S. M. (2022) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000004940 · Cited by 15
Keshtkar, K., Safarpour, A. R., Heshmat, R., Sotoudehmanesh, R., & Keshtkar, A. (2023) · The Turkish Journal of Gastroenterology · doi:10.5152/tjg.2023.22491 · Cited by 15
Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update
Parekh, A., Satish, S., Dulhanty, L., Berzuini, C., & Patel, H. C. (2023) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2023-021107 · Cited by 15
Pan, Y., Lin, Y.-P., Yen, H., Yen, H.-H., Huang, C.-C., Hsieh, H., Janssen, S. J., Hu, M., Lin, W., & Groot, O. Q. (2024) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003030 · Cited by 15
Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis
Zobeiri, A., Rezaee, A., Hajati, F., Argha, A., & Alinejad‐Rokny, H. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105659 · Cited by 16
Vidal, R., Grotle, M., Johnsen, M. B., Yvernay, L., Hartvigsen, J., Ostelo, R., Kjønø, L. G., Enstad, C. L., Killingmo, R. M., Halsnes, E. H., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111593 · Cited by 16
Zhou, Z., Xu, Q., Yi, J., Li, Y., Zhang, S., & Li, W. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103150 · Cited by 15
Diagnostic and Prognostic Research
Bouter, L. M., Zielhuis, G. A., & Zeegers, M. P. (2017) · Bohn Stafleu van Loghum eBooks · doi:10.1007/978-90-368-1741-7_9 · Cited by 15
Siriyotha, S., Tantrakul, V., Plitphonganphim, S., Rattanasiri, S., & Thakkinstian, A. (2021) · Diagnostics · doi:10.3390/diagnostics11061097 · Cited by 15
Huang, Q., Hu, Y.-C., Wang, C.-K., Huang, J., Shen, M., & Ren, L. (2022) · Biological Research For Nursing · doi:10.1177/10998004221131993 · Cited by 16
Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review
Rankin, D. A., Peetluk, L. S., Deppen, S. A., Slaughter, J. C., Katz, S. E., Halasa, N., & Khankari, N. K. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-067878 · Cited by 15
Yoong, S. Q., Bhowmik, P., Kapparath, S., & Porock, D. C. (2024) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djae036 · Cited by 14
Haris, M., Raveendra, K., Travlos, C. K., Lewington, A. J. P., Wu, J., Shuweihdi, F., Nadarajah, R., & Gale, C. P. (2024) · Clinical Kidney Journal · doi:10.1093/ckj/sfae098 · Cited by 14
Oakley, W., Tandle, S., Perkins, Z., & Marsden, M. E. R. (2024) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0000000000004385 · Cited by 15
Jacquemyn, X., Onsem, E. V., Dufendach, K., Brown, J. A., Kliner, D., Toma, C., Serna-Gallegos, D. R., Sá, M. P., & Sultan, I. S. (2024) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2024.05.017 · Cited by 14
Fares, M. Y., Liu, H. H., Etges, A. P. B. D. S., Zhang, Y. B., Warner, J. J. P., Olson, J. J., Fedorka, C. J., Khan, A. Z., Best, M. J., Kirsch, J. M., et al. (2024) · JBJS Reviews · doi:10.2106/jbjs.rvw.24.00075 · Cited by 14
Prediction models of intravenous glucocorticoids therapy response in thyroid eye disease
Zhang, H., Wu, S., Hu, S., Fan, X., Song, X., Feng, T., & Zhou, H. (2024) · European Thyroid Journal · doi:10.1530/etj-24-0122 · Cited by 14
Battle, C. E., Cole, E. M., Carter, K., & Baker, E. J. (2024) · BMC Emergency Medicine · doi:10.1186/s12873-024-01107-6 · Cited by 14
Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis
Tahir, N., Jung, C., Lee, S., Azizah, N., Ho, W., & Li, T. (2025) · Journal of Medical Internet Research · doi:10.2196/65708 · Cited by 16
Furukawa, T. A., Debray, T. P. A., Akechi, T., Yamada, M., Kato, T., Seo, M., & Efthimiou, O. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.141 · Cited by 14
Self-citationPrediction of mortality in very low birth weight neonates in Spain
Iriondo, M., Thió, M., del Río, R., Baucells, B. J., Bosio, M., & Aloy, J. F. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235794 · Cited by 14
Montalto, C., Munafò, A. R., Arzuffi, L., Casula, M., Mirizzi, A. M., Costa, F., Leonardi, S., Visconti, L. O., & Ferlini, M. (2022) · Hellenic Journal of Cardiology · doi:10.1016/j.hjc.2022.04.008 · Cited by 15
Yen, H., Hu, M., Zijlstra, H., Groot, O. Q., Hsieh, H., Yang, J.-J., Karhade, A. V., Chen, P.-C., Chen, Y., Huang, P., et al. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.08.029 · Cited by 14
Predicción de mortalidad en recién nacidos prematuros. Revisión sistemática actualizada
del Río, R., Thió, M., Bosio, M., Figueras, J., & Iriondo, M. (2020) · Anales de Pediatría · doi:10.1016/j.anpedi.2019.11.003 · Cited by 17
Lai, N. M., Gerard, J. P., Ngim, C. F., Kamar, A. A., & Chen, K. (2021) · Neonatology · doi:10.1159/000519497 · Cited by 14
Tiberio, P., Gaudio, M., Belloni, S., Pindilli, S., Benvenuti, C., Jacobs, F., Saltalamacchia, G., Zambelli, A., Santoro, A., & Sanctis, R. D. (2023) · Cancers · doi:10.3390/cancers15133424 · Cited by 13
Rayner, D. G., Kim, B. Y., & Foroutan, F. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111360 · Cited by 15
Li, J., Li, G., Liu, Z., Yang, X., & Yang, Q. (2024) · American Journal of Infection Control · doi:10.1016/j.ajic.2024.07.006 · Cited by 13
Prediction models for postpartum stress urinary incontinence: A systematic review
Wang, L., Zhang, M., Sha, K., Qiao, Y., & Dong, Q. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37988 · Cited by 13
Walker, H., Day, S., Grant, C. H., Jones, C., Ker, R., Sullivan, M. K., Jani, B., Gallacher, K., & Mark, P. B. (2024) · BMC Medicine · doi:10.1186/s12916-024-03649-9 · Cited by 13
Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis
Gao, E. Y., Tan, B. K. J., Tan, N. K. W., Ng, A. C. W., Leong, Z. H., Phua, C. Q., Loh, S. R. H., Uataya, M., Goh, L. C., Ong, T. H., et al. (2024) · Sleep And Breathing · doi:10.1007/s11325-024-03173-3 · Cited by 13
Prediction models for treatment response in migraine: a systematic review and meta-analysis
Chen, Q., Zhang, J., Cao, B., Hu, Y., Kong, Y., Li, B., & Liu, L. (2025) · The Journal of Headache and Pain · doi:10.1186/s10194-025-01972-x · Cited by 13
Doubly Robust Regression Analysis for Data Fusion
Evans, K., Sun, B., Robins, J. M., & Tchetgen, E. J. T. (2019) · Statistica Sinica · doi:10.5705/ss.202018.0334 · Cited by 13
Systematic Review of Prediction Models for Preterm Birth Using CHARMS
Kim, J., & Lee, J. Y. (2021) · Biological Research For Nursing · doi:10.1177/10998004211025641 · Cited by 14
Pineda‐Moncusí, M., Elhussein, L., Delmestri, A., Cooper, C. C., Moayyeri, A., Libanati, C. R., Tóth, E., Prieto‐Alhambra, D., & Khalid, S. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4648 · Cited by 13
The theory and practice of pressure ulcer/injury risk assessment: a critical discussion
Kottner, J., & Coleman, S. B. (2023) · Journal of Wound Care · doi:10.12968/jowc.2023.32.9.560 · Cited by 13
Dhana, A. V., Gupta, R. K., Hamada, Y., Kengne, A. P., Kerkhoff, A. D., Yoon, C., Cattamanchi, A., Reeve, B. W. P., Theron, G., Ndlangalavu, G., et al. (2023) · European Respiratory Review · doi:10.1183/16000617.0021-2023 · Cited by 12
Issanov, A., Aravindakshan, A., Puil, L. J., Tammemägi, M. C., Lam, S. C., & Dummer, T. (2024) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00166-4 · Cited by 14
Clinical prognostic factors for older people: A systematic review and meta-analysis
Veronese, N., Fazzari, A., Armata, M. G., Parisi, A., Parrinello, A., Petralia, V., Saccaro, C., Vesco, M., Tagliaferri, F., Fittipaldo, V. A., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102345 · Cited by 12
Allotey, J., Archer, L., Coomar, D., Snell, K. I. E., Smuk, M., Oakey, L. A., Haqnawaz, S., Betrán, A. P., Chappell, L. C., Ganzevoort, W., et al. (2023) · Health Technology Assessment · doi:10.3310/dabw4814 · Cited by 12
Self-citationMarzano, L. A., Husain‐Syed, F., Reis, T. D. A., Ronco, C., & Zanella, M. (2022) · Journal of Human Hypertension · doi:10.1038/s41371-022-00731-8 · Cited by 12
Prediction of mortality in premature neonates. An updated systematic review
del Río, R., Thió, M., Bosio, M., Figueras, J., & Iriondo, M. (2020) · Anales de Pediatría (English Edition) · doi:10.1016/j.anpede.2019.11.005 · Cited by 12
Ko, S. Q., Valsdottir, L. R., Strom, J. B., Cheng, Y., Hirayama, A., Liu, P., Yanagisawa, N., Yen, H., Shen, C., & Yeh, R. W. (2018) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2018.08.025 · Cited by 12
Reporting Standards and Critical Appraisal of Prediction Models
Wee, L., van Kuijk, S. M. J., Dankers, F. J. W. M., Traverso, A., Welch, M. L., & Dekker, A. L. A. J. (2018) · Springer eBooks · doi:10.1007/978-3-319-99713-1_10 · Cited by 14
Huang, C.-C., Peng, K.-P., Hsieh, H., Groot, O. Q., Yen, H., Tsai, C., Karhade, A. V., Lin, Y., Kao, Y.-T., Yang, J.-J., et al. (2023) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000002706 · Cited by 11
Lee, T., Chen, Y., Groot, O. Q., Yen, H., Bindels, B. J. J., Pierik, R., Hsieh, H., Karhade, A. V., Tseng, T., Lai, Y., et al. (2023) · Cancer Medicine · doi:10.1002/cam4.6097 · Cited by 11
Xu, X., Jiang, L., Zeng, Y., Pan, L., Lou, Z., & Ruan, B. (2023) · Virology Journal · doi:10.1186/s12985-023-02145-5 · Cited by 11
Herrera, D. J., van de Veerdonk, W., Seibert, D. M., Boke, M. M., Gutiérrez‐Ortiz, C., Yimer, N. B., Feyen, K., Ferrari, A., & Hal, G. F. V. (2023) · Gastrointestinal Disorders · doi:10.3390/gidisord5040045 · Cited by 11
Risk prediction model for postoperative pneumonia in esophageal cancer patients: A systematic review
Jiang, Y., Li, Z., Jiang, W., Wei, T., & Chen, B. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1419633 · Cited by 11
Risk Prediction Models as an Emerging Trend for Managing Cancer‐Related Fatigue: A Systematic Review
Zhang, Y., Li, L., Li, X., Zhang, S., Zhou, L., Chen, X., & Hu, X. (2024) · Journal of Advanced Nursing · doi:10.1111/jan.16680 · Cited by 11
Sharif, A. F., Kasemy, Z. A. A., Alshalawi, K. S., & Sobh, Z. K. (2025) · Critical Reviews in Toxicology · doi:10.1080/10408444.2024.2445021 · Cited by 11
Gupta, A. K., Mustafiz, C., Mutahar, D., Zaka, A., Parvez, R., Mridha, N., Stretton, B., Kovoor, J. G., Bacchi, S., Ramponi, F., et al. (2025) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2025.01.037 · Cited by 11
Araújo, A. L. D., Santos‐Silva, A. R., & Kowalski, L. P. (2023) · Current Oncology Reports · doi:10.1007/s11912-023-01365-w · Cited by 11
Gates, M., Pillay, J., Thériault, G., Limburg, H., Grad, R. M., Klarenbach, S. W., Korownyk, C. S., Reynolds, D. L., Riva, J. J., Thombs, B. D., et al. (2019) · Systematic Reviews · doi:10.1186/s13643-019-1094-5 · Cited by 12
Xu, W., Huang, J., Yu, Q., Yu, H., Pu, Y., & Shi, Q. (2021) · Neurological Sciences · doi:10.1007/s10072-021-05219-w · Cited by 11
Guidance to best tools and practices for systematic reviews
Kolaski, K., Logan, L. R., & Ioannidis, J. P. A. (2023) · Acta Anaesthesiologica Scandinavica · doi:10.1111/aas.14295 · Cited by 11
Suliman, A., Masud, M. M., Serhani, M. A., Abdullahi, A. S., & Oulhaj, A. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-082654 · Cited by 10
Lee, C., Chen, C., Yen, H., Lin, Y.-P., Lai, C., Wang, J., Groot, O. Q., Janssen, S. J., Schwab, J. H., Hsu, F., et al. (2024) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000003185 · Cited by 10
Shrivastav, V. N., Yogesh, M., Ramachandran, A., Makwana, N. R., & Parmar, D. (2025) · BMC Infectious Diseases · doi:10.1186/s12879-025-10941-3 · Cited by 10
Moro, F., Momi, M., Ledger, A., Barreñada, L., Ceusters, J., Sturla, D., Mor, E., Fornari, L., Mascilini, F., Ciccarone, F., et al. (2025) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2025.07.017 · Cited by 10
Liu, L., Zhou, P., Hou, L., Kao, C., Zhang, Z., Wang, D., Yu, L., Wang, F., Wang, Y., & Yu, Z. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2534522 · Cited by 10
Zhong, J., Si, L., Zhang, G., Huo, J., Xing, Y., Hu, Y., Zhang, H., & Yao, W. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01683-9 · Cited by 10
Gao, Y., Chen, Y., Hu, M., Gan, T., Sun, X., Zhang, Z., He, W., & Wu, X. (2022) · Journal of Applied Gerontology · doi:10.1177/07334648221097084 · Cited by 11
Nomali, M., Khalili, D., Yaseri, M., Mansournia, M. A., Ayati, A., Navid, H., & Nedjat, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0292396 · Cited by 11
Geethadevi, G. M., Quinn, T., George, J., Anstey, K. J., Bell, J. S., & Cross, A. J. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd014885 · Cited by 10
Tian, Y., Chen, W., Zhou, T., Li, J., Ding, K., & Li, J. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104173 · Cited by 10
Askar, M., Tafavvoghi, M., Småbrekke, L., Bongo, L. A., & Svendsen, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309175 · Cited by 9
Hesseling, B., Prinsze, N., van Etten‐Jamaludin, F. S., Perry, S. I. B., Eygendaal, D. E., Mathijssen, N. M. C., & Snoeker, B. A. M. (2024) · Systematic Reviews · doi:10.1186/s13643-024-02694-y · Cited by 9
Lu, W., Wang, N., Fang, X., Yang, H., He, H., Qin, D., & Hua, F. (2025) · Journal of Dentistry · doi:10.1016/j.jdent.2025.105686 · Cited by 9
When a meta-analysis can be really useful?
Basile, C., Villaschi, A., & Maggioni, A. P. (2025) · International Journal of Cardiology · doi:10.1016/j.ijcard.2025.133423 · Cited by 9
Molica, S., & Giannarelli, D. (2020) · Leukemia · doi:10.1038/s41375-020-0924-8 · Cited by 9
RetractedStanley, C., Lennon, D. T., Moran, C. J., Vasireddy, A., & Rowan, F. E. (2023) · Injury · doi:10.1016/j.injury.2023.111017 · Cited by 9
Wu, W., Deng, Z., Wahafu, A., Wang, Y., Xiang, J., Zhu, L., Li, B., Wang, M., & Wang, J. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01750 · Cited by 9
Protocol for a systematic review of prognostic models for recurrent events in chronic conditions
Watson, V., Smith, C. T., & Bonnett, L. J. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-0070-9 · Cited by 9
Clephas, P. R. D., Hoeks, S. E., Trivella, M., Guay, C. S., Singh, P. M., Klimek, M., & Heesen, M. A. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-051554 · Cited by 9
Villalaín, C., Moon‐Grady, A. J., Herberg, U., Strainic, J. P., Cohen, J. L., Shah, A. M., Levi, D. S., Gómez‐Montes, E., Herraı̀z, I., & Galindo, A. (2023) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.26176 · Cited by 8
Lifestyle predictors of colorectal cancer in European populations: a systematic review
Mertens, E., Keuchkarian, M., Vasquez, M. S., Vandevijvere, S., & Peñalvo, J. L. (2024) · BMJ Nutrition Prevention & Health · doi:10.1136/bmjnph-2022-000554 · Cited by 8
Rijk, M. H., Platteel, T. N., van den Berg, T. M. C., Geersing, G., Little, P. S., Rutten, F. H., van Smeden, M., & Venekamp, R. P. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-075475 · Cited by 8
Travlos, C. K., Chirgwin-Dasgupta, A., Trinh, E., Sniderman, A. D., Alam, A., & Mavrakanas, T. A. (2024) · Clinical Kidney Journal · doi:10.1093/ckj/sfae347 · Cited by 8
Hillier, B., Scandrett, K., Coombe, A., Hernandez‐Boussard, T., Steyerberg, E. W., Takwoingi, Y., Veličković, V. M., & Dinnes, J. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00182-4 · Cited by 8
Risk prediction models for ovarian hyperstimulation syndrome: a systematic review and meta-analysis
Liu, J., Liu, F., Xu, W., Zhang, L., Tian, Q., Du, L., Li, S., & Zhang, M. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-07971-9 · Cited by 8
Predictive Models for Mortality in Cardiogenic Shock: A Systematic Review and Meta-Analysis
Ng, G. W. Y., Low, C. J. W., Ng, K. J., Ling, R. R., Chan, S. P., Anbalakan, K., Jung, C., Lin, W., Pilcher, D., Shekar, K., et al. (2025) · Critical Care Medicine · doi:10.1097/ccm.0000000000006937 · Cited by 8
Kim, S., Zhou, Y., Guo, Y., Xiao, C., & Zheng, K. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag045 · Cited by 8
Metadata
- Authors
- 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.
- Affiliations
- University Medical Center Utrecht (NL)
- Keele University (GB)
- Corresponding author
- Debray, Thomas P A
- ORCID
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2017-01-05
- Volume
- 356
- Pages
- i6460
- ISSN
- 1756-1833 (ISSN-L)
- OpenAlex
- W2570878489
- Cited by
- 657
- References
- 48
- Citations by year
- 2026: 109 · 2025: 98 · 2024: 94 · 2023: 81 · 2022: 64 · 2021: 82 · 2020: 56 · 2019: 33 · 2018: 27 · 2017: 11
- FWCI
- 8.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Keele Research Repository (Keele University) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- Medical Research Council (MR/L004933/1, G0902393)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Keywords
- prediction model validation, systematic reviews, meta-analysis, predictive performance, discrimination (statistics), calibration, EuroSCORE, postoperative mortality, coronary artery bypass grafting, risk prediction model
- MeSH terms
- Systematic Reviews as Topic, Coronary Artery Bypass, Decision Support Techniques, Humans, Meta-Analysis as Topic, Risk Assessment, Validation Studies as Topic
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