World's Best Scientists 2026 revealed!
B. Van Calster

B. Van Calster

D-Index & Metrics

Medicine

D-Index
82
Citations
27816
World Ranking
15995
National Ranking
200

B. Van Calster publication distribution in Medicine in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Medicine in 2026. The highlighted bar marks where B. Van Calster sits on this spectrum.

101–120 publications: 5 scientists 121–140 publications: 26 scientists 141–160 publications: 73 scientists 161–180 publications: 155 scientists 181–200 publications: 230 scientists 201–220 publications: 363 scientists 221–240 publications: 487 scientists 241–260 publications: 545 scientists 261–280 publications: 722 scientists 281–300 publications: 768 scientists 301–320 publications: 834 scientists 321–340 publications: 895 scientists 341–360 publications: 922 scientists 361–380 publications: 838 scientists 381–400 publications: 861 scientists 401–420 publications: 918 scientists 421–440 publications: 806 scientists 441–460 publications: 771 scientists 461–480 publications: 751 scientists 481–500 publications: 713 scientists 501–520 publications: 617 scientists 521–540 publications: 611 scientists 541–560 publications: 537 scientists 561–580 publications: 504 scientists 581–600 publications: 509 scientists 601–620 publications: 396 scientists 621–640 publications: 386 scientists 641–660 publications: 371 scientists 661–680 publications: 340 scientists 681–700 publications: 336 scientists 701–720 publications: 307 scientists 721–740 publications: 259 scientists 741–760 publications: 230 scientists 761–780 publications: 228 scientists 781–800 publications: 217 scientists 801–820 publications: 204 scientists 821–840 publications: 186 scientists 841–860 publications: 177 scientists 861–880 publications: 155 scientists 881–900 publications: 139 scientists 901–920 publications: 145 scientists 921–940 publications: 116 scientists 941–960 publications: 133 scientists 961–980 publications: 91 scientists 981–1,000 publications: 96 scientists 1,001–1,020 publications: 77 scientists 1,021–1,040 publications: 70 scientists 1,041–1,060 publications: 63 scientists 1,061–1,080 publications: 77 scientists 1,081–1,100 publications: 49 scientists 1,101–1,120 publications: 54 scientists 1,121–1,140 publications: 49 scientists 1,141–1,160 publications: 51 scientists 1,161–1,180 publications: 35 scientists 1,181–1,200 publications: 39 scientists 1,201–1,220 publications: 26 scientists 1,221–1,240 publications: 37 scientists 1,241–1,260 publications: 36 scientists 1,261–1,280 publications: 27 scientists 1,281–1,300 publications: 32 scientists 1,301–1,320 publications: 28 scientists 1,321–1,340 publications: 17 scientists 1,341–1,360 publications: 30 scientists 1,361–1,380 publications: 28 scientists 1,381–1,400 publications: 17 scientists 1,401–1,420 publications: 21 scientists 1,421–1,440 publications: 15 scientists 1,441–1,460 publications: 12 scientists 1,461–1,480 publications: 12 scientists 1,481–1,500 publications: 18 scientists 1,501–1,520 publications: 14 scientists 1,521–1,540 publications: 17 scientists 1,541–1,560 publications: 15 scientists 1,561–1,580 publications: 6 scientists 1,581–1,600 publications: 2 scientists 1,601–1,620 publications: 12 scientists 1,621–1,640 publications: 11 scientists 1,641–1,660 publications: 8 scientists 1,661–1,680 publications: 5 scientists 1,681–1,700 publications: 5 scientists 1,701–1,720 publications: 10 scientists 1,721–1,740 publications: 12 scientists 1,741–1,760 publications: 14 scientists 1,761–1,780 publications: 6 scientists 1,781–1,795 publications: 5 scientists 1,796+ publications: 100 scientists
101 publications 1,796+

This scientist: 352 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,796 publications or more.

B. Van Calster D-index placement in Medicine in 2026

The chart shows the D-index (discipline H-index) distribution of Medicine scientists ranked by Research.com in 2026. The highlighted bar marks where B. Van Calster sits on this spectrum.

70–71 D-Index: 226 scientists 72–73 D-Index: 391 scientists 74–75 D-Index: 574 scientists 76–77 D-Index: 730 scientists 78–79 D-Index: 891 scientists 80–81 D-Index: 972 scientists 82–83 D-Index: 1,027 scientists 84–85 D-Index: 1,003 scientists 86–87 D-Index: 960 scientists 88–89 D-Index: 970 scientists 90–91 D-Index: 922 scientists 92–93 D-Index: 839 scientists 94–95 D-Index: 808 scientists 96–97 D-Index: 779 scientists 98–99 D-Index: 668 scientists 100–101 D-Index: 611 scientists 102–103 D-Index: 630 scientists 104–105 D-Index: 513 scientists 106–107 D-Index: 541 scientists 108–109 D-Index: 445 scientists 110–111 D-Index: 429 scientists 112–113 D-Index: 400 scientists 114–115 D-Index: 393 scientists 116–117 D-Index: 318 scientists 118–119 D-Index: 302 scientists 120–121 D-Index: 287 scientists 122–123 D-Index: 255 scientists 124–125 D-Index: 256 scientists 126–127 D-Index: 252 scientists 128–129 D-Index: 230 scientists 130–131 D-Index: 179 scientists 132–133 D-Index: 168 scientists 134–135 D-Index: 163 scientists 136–137 D-Index: 159 scientists 138–139 D-Index: 134 scientists 140–141 D-Index: 134 scientists 142–143 D-Index: 118 scientists 144–145 D-Index: 109 scientists 146–147 D-Index: 106 scientists 148–149 D-Index: 74 scientists 150–151 D-Index: 79 scientists 152–153 D-Index: 80 scientists 154–155 D-Index: 87 scientists 156–157 D-Index: 57 scientists 158–159 D-Index: 74 scientists 160–161 D-Index: 69 scientists 162–163 D-Index: 60 scientists 164–165 D-Index: 53 scientists 166–167 D-Index: 39 scientists 168–169 D-Index: 42 scientists 170–171 D-Index: 32 scientists 172–173 D-Index: 39 scientists 174–175 D-Index: 40 scientists 176–177 D-Index: 28 scientists 178–179 D-Index: 19 scientists 180–181 D-Index: 23 scientists 182–183 D-Index: 31 scientists 184–185 D-Index: 18 scientists 186–187 D-Index: 20 scientists 188–189 D-Index: 22 scientists 190–191 D-Index: 13 scientists 192–193 D-Index: 21 scientists 194–195 D-Index: 12 scientists 196–197 D-Index: 12 scientists 198–199 D-Index: 14 scientists 200–201 D-Index: 15 scientists 202–203 D-Index: 13 scientists 204–205 D-Index: 10 scientists 206–207 D-Index: 8 scientists 208–209 D-Index: 4 scientists 210–211 D-Index: 12 scientists 212–213 D-Index: 11 scientists 214–215 D-Index: 10 scientists 216 D-Index: 4 scientists 217+ D-Index: 98 scientists
70 D-Index 217+

This scientist: 82 D-Index — 21st percentile

21% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 217 D-Index or more.

Overview

B. Van Calster is affiliated with KU Leuven in Belgium, where their research primarily focuses on medicine with an emphasis on artificial intelligence applications, statistics, obstetrics, gynecology, reproductive medicine, and surgery. Their work spans several interconnected fields, reflecting a multidisciplinary approach.

The scientist's recent publications illustrate a concentration on clinical prediction models and artificial intelligence in healthcare. Key papers include:

  • Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal, 2020, BMJ
  • TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, 2024, BMJ
  • 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, 2021, BMJ Open
  • Interpreting area under the receiver operating characteristic curve, 2022, The Lancet Digital Health
  • Randomized Trial of Fetal Surgery for Severe Left Diaphragmatic Hernia, 2021, New England Journal of Medicine

B. Van Calster frequently collaborates with several coauthors, highlighting the interconnected nature of their research projects. Frequent collaborators include:

  • D. Timmerman
  • Laure Wynants
  • Gary S. Collins
  • Maarten van Smeden
  • T. Bourne

The scientist has a notable publication record in specific venues, indicating preferred platforms for disseminating research findings. These include:

  • Ultrasound in Obstetrics and Gynecology
  • Journal of Clinical Epidemiology
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • BMJ

B. Van Calster's subfields reflect the diverse aspects of their research focus, covering:

  • Artificial Intelligence
  • Statistics and Probability
  • Obstetrics and Gynecology
  • Reproductive Medicine
  • Surgery

Their main research topics provide further detail on the specific themes explored within their work, including:

  • Machine Learning in Healthcare
  • Ovarian cancer diagnosis and treatment
  • Endometrial and Cervical Cancer Treatments
  • Artificial Intelligence in Healthcare and Education
  • Meta-analysis and systematic reviews
  • Sepsis Diagnosis and Treatment
  • Statistical Methods in Clinical Trials

Best Publications

  • A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

    Evangelia Christodoulou;Jie Ma;Gary S. Collins;Ewout W. Steyerberg

  • Calibration: the Achilles heel of predictive analytics

    Ben Van Calster;Ben Van Calster;David J. McLernon;Maarten van Smeden;Laure Wynants;Laure Wynants

  • Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests.

    Andrew J Vickers;Ben Van Calster;Ben Van Calster;Ewout W Steyerberg

  • Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators.

    Ben Van Calster;Ben Van Calster;Laure Wynants;Jan F.M. Verbeek;Jan Y. Verbakel;Jan Y. Verbakel

  • A simple, step-by-step guide to interpreting decision curve analysis.

    Andrew J. Vickers;Ben van Calster;Ben van Calster;Ewout W. Steyerberg

  • Simple ultrasound-based rules for the diagnosis of ovarian cancer

    D Timmerman;Antonia Carla Testa;T Bourne;L Ameye

  • A calibration hierarchy for risk models was defined: from utopia to empirical data.

    Ben Van Calster;Ben Van Calster;Daan Nieboer;Yvonne Vergouwe;Bavo De Cock

  • Logistic Regression Model to Distinguish Between the Benign and Malignant Adnexal Mass Before Surgery: A Multicenter Study by the International Ovarian Tumor Analysis Group

    Dirk Timmerman;Antonia Carla Testa;Tom Bourne;Enrico Ferrazzi

  • 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.

    Gary S Collins;Gary S Collins;Paula Dhiman;Paula Dhiman;Constanza L Andaur Navarro;Jie Ma

  • Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study.

    Ben Van Calster;Kirsten Van Hoorde;Lil Valentin;Antonia C Testa

  • Pregnancy of unknown location: a consensus statement of nomenclature, definitions, and outcome

    Kurt Barnhart;Norah M. van Mello;Tom Bourne;Tom Bourne;Emma Kirk

  • Long-term cognitive and cardiac outcomes after prenatal exposure to chemotherapy in children aged 18 months or older: an observational study

    Frédéric Amant;Kristel Van Calsteren;Michael J Halaska;Mina Mhallem Gziri

  • Prognosis of Women With Primary Breast Cancer Diagnosed During Pregnancy: Results From an International Collaborative Study

    Frédéric Amant;Gunter von Minckwitz;Sileny N. Han;Marijke Bontenbal

  • Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

    Dirk Timmerman;Ben Van Calster;Antonia Testa;Luca Savelli

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

    Ben Van Calster;Andrew J. Vickers

  • Treatment of breast cancer during pregnancy: an observational study

    Sibylle Loibl;Sileny N Han;Gunter von Minckwitz;Marijke Bontenbal

  • Oncological management and obstetric and neonatal outcomes for women diagnosed with cancer during pregnancy: a 20-year international cohort study of 1170 patients

    Jorine de Haan;Jorine de Haan;Magali Verheecke;Kristel Van Calsteren;Ben Van Calster

  • Endometriomas: their ultrasound characteristics

    C. Van Holsbeke;B. Van Calster;S. Guerriero;L. Savelli

  • Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis

    Jeroen Kaijser;Ahmad Sayasneh;Kirsten Van Hoorde;Sadaf Ghaem-Maghami

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

    L Wynants;B Van Calster;Bonten Mmj.;G Collins

Frequent Co-Authors

Tom Bourne
Tom Bourne Imperial College London
Lil Valentin
Lil Valentin Lund University
Patrick Neven
Patrick Neven KU Leuven
Hans Wildiers
Hans Wildiers KU Leuven
Davor Jurkovic
Davor Jurkovic University College London
Thomas D'Hooghe
Thomas D'Hooghe Yale University
Giovanni Scambia
Giovanni Scambia Catholic University of the Sacred Heart

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