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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review
Paula Dhiman, Jie Ma, Constanza L. Andaur Navarro, et al.
Journal of Clinical Epidemiology (2023) Vol. 157, pp. 120-133
Open Access | Times Cited: 24

Showing 24 citing articles:

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
Gary S. Collins, Karel G.M. Moons, Paula Dhiman, et al.
BMJ (2024), pp. e078378-e078378
Open Access | Times Cited: 311
Artificial Intelligence in Orthodontics: Critical Review
Noah F. Nordblom, M. Büttner, Falk Schwendicke
Journal of Dental Research (2024) Vol. 103, Iss. 6, pp. 577-584
Open Access | Times Cited: 16
The application and use of artificial intelligence in cancer nursing: A systematic review
Siobhán O’Connor, Amy Vercell, David Wong, et al.
European Journal of Oncology Nursing (2024) Vol. 68, pp. 102510-102510
Open Access | Times Cited: 13
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
Karel G.M. Moons, Johanna AAG Damen, T. K. Kaul, et al.
BMJ (2025), pp. e082505-e082505
Closed Access | Times Cited: 1
SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models
Constanza L. Andaur Navarro, Johanna AAG Damen, Mona Ghannad, et al.
Journal of Clinical Epidemiology (2024) Vol. 170, pp. 111364-111364
Open Access | Times Cited: 4
Making the black box more transparent: improving the reporting of artificial intelligence studies in healthcare
Gary S. Collins
BMJ (2024), pp. q832-q832
Open Access | Times Cited: 4
Overinterpretation of evaluation results in machine learning studies for maize yield prediction: A systematic review
Joerg Leukel, Luca Scheurer, Tobias Zimpel
Computers and Electronics in Agriculture (2025) Vol. 230, pp. 109892-109892
Open Access
A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality
Brian Critelli, Amier Hassan, Ila Lahooti, et al.
PLoS Medicine (2025) Vol. 22, Iss. 2, pp. e1004432-e1004432
Open Access
Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review
Amier Hassan, Brian Critelli, Ila Lahooti, et al.
Diagnostic and Prognostic Research (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 3
Artificial Intelligence in Cancer Clinical Research: II. Development and Validation of Clinical Prediction Models
Gary H. Lyman, Nicole M. Kuderer
Cancer Investigation (2024) Vol. 42, Iss. 6, pp. 447-451
Open Access | Times Cited: 3
Twelve practical recommendations for developing and applying clinical predictive models
Guoshuang Feng, Huiyu Xu, Shibiao Wan, et al.
The Innovation Medicine (2024), pp. 100105-100105
Closed Access | Times Cited: 3
Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study
Ash Kieran Clift, Hadley Mahon, Ghazanfar Khan, et al.
British Journal of Cancer (2024) Vol. 131, Iss. 2, pp. 305-311
Open Access | Times Cited: 2
Identifying and Minimizing Incentives for Competing Interests in Sports Medicine Publications
Ian Shrier, Franco M. Impellizzeri, Steven D. Stovitz
Sports Medicine (2024) Vol. 54, Iss. 8, pp. 1991-2000
Closed Access | Times Cited: 1
Artificial intelligence in peri‐operative prediction model research: are we there yet?
Akshay Shah, Paula Dhiman
Anaesthesia (2024) Vol. 79, Iss. 10, pp. 1017-1022
Closed Access | Times Cited: 1
PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma
Jiangdian Song, Jie Zhang, Guichao Liu, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Ethical guidance for reporting and evaluating claims of AI outperforming human doctors
Jojanneke Drogt, Megan Milota, Anne van den Brink, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 1
A systematic review of cardiac surgery clinical prediction models that include intra-operative variables
Ceri Jones, Marcus Taylor, Matthew Sperrin, et al.
Perfusion (2024)
Open Access
A Systematic Review of Machine Learning-based Prognostic Models for Acute Pancreatitis: Towards Improving Methods and Reporting Quality
Brian Critelli, Amier Hassan, Ila Lahooti, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes
Bernardo Paim de Mattos, Christian Mattjie, Rafaela Ravazio, et al.
International Journal of Mental Health and Addiction (2024)
Open Access
Quality and completeness of, and spin in reporting of, pilot and feasibility studies in hip and knee arthroplasty: a protocol for a methodological survey
Zinnia Chung, Lawrence Mbuagbaw, Lipalo Mokete, et al.
BMJ Open (2024) Vol. 14, Iss. 10, pp. e085441-e085441
Open Access
Public Disclosure of Results from Artificial Intelligence/Machine Learning Research in Healthcare since 2010: Cross-sectional Analysis (Preprint)
Shoko Maru, Ryohei Kuwatsuru, Matthias Michael, et al.
Journal of Medical Internet Research (2024)
Open Access
Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023) (Preprint)
Shoko Maru, Ryohei Kuwatsuru, Matthias Michael, et al.
(2024)
Closed Access
The authors reply
Agathe Truchot, Marc Raynaud, Alexandre Loupy
Kidney International (2023) Vol. 104, Iss. 5, pp. 1036-1036
Closed Access
Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study
Zhe Jin, Fengmei Ma, Haoyang Chen, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access
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