Karel Moons

3.6k total citations · 3 hit papers
19 papers, 2.6k citations indexed

About

Karel Moons is a scholar working on Surgery, Cardiology and Cardiovascular Medicine and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Karel Moons has authored 19 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Surgery, 4 papers in Cardiology and Cardiovascular Medicine and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Karel Moons's work include Statistical Methods and Bayesian Inference (2 papers), Machine Learning in Healthcare (2 papers) and Statistical Methods and Inference (2 papers). Karel Moons is often cited by papers focused on Statistical Methods and Bayesian Inference (2 papers), Machine Learning in Healthcare (2 papers) and Statistical Methods and Inference (2 papers). Karel Moons collaborates with scholars based in Netherlands, United Kingdom and Germany. Karel Moons's co-authors include James R. Carpenter, Ian R. White, Douglas G. Altman, A. Rogier T. Donders, Rolf H. H. Groenwold, Pratik P. Pandharipande, Ayumi Shintani, E. Wesley Ely, Ramona O. Hopkins and Timothy D. Girard and has published in prestigious journals such as The Lancet, PLoS ONE and Cancer.

In The Last Decade

Karel Moons

18 papers receiving 2.5k citations

Hit Papers

Long-Term Cognitive Impairment After Critical Illness 2013 2026 2017 2021 2014 2013 2021 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Karel Moons Netherlands 11 826 385 361 318 317 19 2.6k
Phil Alderson United Kingdom 25 924 1.1× 374 1.0× 213 0.6× 609 1.9× 416 1.3× 42 2.8k
N. David Yanez United States 28 502 0.6× 416 1.1× 295 0.8× 265 0.8× 469 1.5× 59 2.6k
Abdulla A. Damluji United States 23 801 1.0× 305 0.8× 428 1.2× 653 2.1× 1.2k 3.7× 143 3.1k
Johanna C. Korevaar Netherlands 51 1.5k 1.9× 628 1.6× 597 1.7× 1.1k 3.3× 572 1.8× 104 7.6k
Miriam M. Treggiari United States 34 935 1.1× 741 1.9× 625 1.7× 608 1.9× 550 1.7× 140 4.2k
Tjeerd van der Ploeg Netherlands 26 545 0.7× 337 0.9× 296 0.8× 639 2.0× 495 1.6× 72 2.5k
Mayur B. Patel United States 34 1.9k 2.3× 690 1.8× 746 2.1× 793 2.5× 315 1.0× 140 4.0k
Ryan M. Carnahan United States 32 490 0.6× 368 1.0× 230 0.6× 278 0.9× 855 2.7× 154 3.7k
Ralf Kuhlen Germany 32 633 0.8× 428 1.1× 308 0.9× 500 1.6× 429 1.4× 191 3.2k
Michael J. Breslow United States 34 1.1k 1.3× 646 1.7× 380 1.1× 1.4k 4.3× 1.3k 4.0× 93 4.2k

Countries citing papers authored by Karel Moons

Since Specialization
Citations

This map shows the geographic impact of Karel Moons's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Karel Moons with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karel Moons more than expected).

Fields of papers citing papers by Karel Moons

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Karel Moons. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Karel Moons. The network helps show where Karel Moons may publish in the future.

Co-authorship network of co-authors of Karel Moons

This figure shows the co-authorship network connecting the top 25 collaborators of Karel Moons. A scholar is included among the top collaborators of Karel Moons based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Karel Moons. Karel Moons is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Leeuwenberg, Artuur, Anne de Hond, David Jenkins, et al.. (2024). Updating methods for artificial intelligence–based clinical prediction models: a scoping review. Journal of Clinical Epidemiology. 178. 111636–111636. 2 indexed citations
2.
Navarro, Constanza L. Andaur, Johanna AAG Damen, Toshihiko Takada, et al.. (2021). Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ. 375. n2281–n2281. 222 indexed citations breakdown →
3.
Roes, Kit C. B., et al.. (2018). Outlier classification performance of risk adjustment methods when profiling multiple providers. BMC Medical Research Methodology. 18(1). 54–54.
4.
Vermeulen, Roel, et al.. (2016). Expected number of asbestos-related lung cancers in the Netherlands in the next two decades: a comparison of methods. Occupational and Environmental Medicine. 73(5). 342–349. 4 indexed citations
5.
Smit, Henriëtte A., Mariona Pinart, Josep M. Antó, et al.. (2015). Childhood asthma prediction models: a systematic review. The Lancet Respiratory Medicine. 3(12). 973–984. 66 indexed citations
6.
Pandharipande, Pratik P., Timothy D. Girard, James C. Jackson, et al.. (2014). Long-Term Cognitive Impairment After Critical Illness. Survey of Anesthesiology. 58(5). 212–213. 905 indexed citations breakdown →
7.
Hingorani, Aroon D., Daniëlle van der Windt, Richard D Riley, et al.. (2013). Prognosis research strategy (PROGRESS) 4: Stratified medicine research. BMJ. 346(feb05 1). e5793–e5793. 347 indexed citations breakdown →
8.
Dijk, Diederik van, Erik W.L. Jansen, Ron Hijman, et al.. (2013). Cognitive Outcome After Off-Pump and On-Pump Coronary Artery Bypass Graft Surgery. 6 indexed citations
9.
Abbasi, Ali, Linda M. Peelen, Eva Corpeleijn, et al.. (2012). Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 345(sep18 2). e5900–e5900. 223 indexed citations
10.
Geersing, Geert‐Jan, Walter Bouwmeester, Peter Zuithoff, et al.. (2012). Search Filters for Finding Prognostic and Diagnostic Prediction Studies in Medline to Enhance Systematic Reviews. PLoS ONE. 7(2). e32844–e32844. 270 indexed citations
11.
Groenwold, Rolf H. H., Ian R. White, A. Rogier T. Donders, et al.. (2012). Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. Canadian Medical Association Journal. 184(11). 1265–1269. 334 indexed citations
12.
Svircevic, Vesna, Arno P. Nierich, Karel Moons, et al.. (2011). Thoracic Epidural Anesthesia for Cardiac Surgery: A Randomized Trial. Anesthesiology. 114(2). 262–270. 52 indexed citations
13.
Koffijberg, Hendrik, Virissa Lenters, Lützen Portengen, et al.. (2011). Lung cancer risk at low asbestos exposure: meta-regression of the exposure-response relationship. Occupational and Environmental Medicine. 68(Suppl_1). A21–A21. 1 indexed citations
14.
Westerhuis, Michelle E.M.H., et al.. (2009). [Intrapartum foetal monitoring: from stethoscope to ST analysis of the ECG].. PubMed. 153. B259–B259. 3 indexed citations
15.
Hemel, Norbert M. van, Willem G. de Voogt, Joan G. Meeder, et al.. (2008). Routine follow-up after pacemaker implantation: frequency, pacemaker programming and professionals in charge. EP Europace. 10(7). 832–837. 18 indexed citations
16.
Klei, Wilton A. van, Diederick E. Grobbee, C. L. G. Rutten, et al.. (2003). Role of history and physical examination in preoperative evaluation. European Journal of Anaesthesiology. 20(8). 612–618. 14 indexed citations
17.
Balt, Jippe C., et al.. (2002). Effect of rewarming speed during hypothermic cardiopulmonary bypass on cerebral pressure–flow relation. Acta Anaesthesiologica Scandinavica. 46(3). 283–288. 8 indexed citations
18.
Cremer, Olaf L., Karel Moons, & Cor J. Kalkman. (2001). Propofol use in head-injury patients. The Lancet. 357(9269). 1709–1710. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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