Banafsheh Arshi, Laure Wynants, Eline Rijnhart, Kelly Reeve, Laura Elizabeth Cowley, Luc J Smits
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By taking random samples for each year, we identified eligible studies that developed a multivariable model (ie, diagnostic or prognostic) for individual-level prediction of a health outcome across all medical fields. Exclusion criteria included development of models with a single predictor, studies not involving humans, methodological studies, conference abstracts, articles with unavailable full text, and those not available in English. We estimated the total and annual number of published regression-based multivariable CPM development articles, based on the total number of publications, proportion of included articles, and the search sensitivity. Furthermore, we used an adjusted Poisson regression to extrapolate our results to the period 1950-2024. Additionally, we estimated the number of articles that developed CPMs using techniques other than regression (eg, machine learning).</p><p><strong>Results: </strong>From a random sample of 10,660 articles published between 1995 and 2020, 109 regression-based CPM development articles were included. We estimated that 82,772 (95% CI 65,313-100,231) CPM development articles using regression were published, with an acceleration in model development from 2010 onward. With the addition of articles that developed non-regression-based CPMs, the number increased to 147,714 (95% CI 125,201-170,226). After extrapolation to the years 1950-2024, the number of articles increased to 156,673 and 248,431 for regression-based models and total CPMs, respectively.</p><p><strong>Conclusions: </strong>Based on a representative sample of publications from the literature, we estimated that nearly 250,000 articles reporting the development of CPMs across all medical fields were published until 2024. CPM development-related publications continue to increase in number. To prevent research waste and close the gap between research and clinical practice, focus should shift away from developing new CPMs to facilitating model validation and impact assessment of the plethora of existing CPMs. Limitations of this study include restriction of search to articles available in English and development of the validated search strategy prior to the popularity of artificial intelligence and machine learning models.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e62710"},"PeriodicalIF":3.1000,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12252138/pdf/","citationCount":"0","resultStr":"{\"title\":\"Number of Publications on New Clinical Prediction Models: A Bibliometric Review.\",\"authors\":\"Banafsheh Arshi, Laure Wynants, Eline Rijnhart, Kelly Reeve, Laura Elizabeth Cowley, Luc J Smits\",\"doi\":\"10.2196/62710\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Concerns have been expressed about the abundance of new clinical prediction models (CPMs) proposed in the literature. However, the extent of this proliferation in prediction research remains unclear.</p><p><strong>Objective: </strong>This study aimed to estimate the total and annual number of CPM development-related publications available across all medical fields.</p><p><strong>Methods: </strong>Using a validated search strategy, we conducted a systematic search of literature for prediction model studies published in Pubmed and Embase between 1995 and the end of 2020. By taking random samples for each year, we identified eligible studies that developed a multivariable model (ie, diagnostic or prognostic) for individual-level prediction of a health outcome across all medical fields. Exclusion criteria included development of models with a single predictor, studies not involving humans, methodological studies, conference abstracts, articles with unavailable full text, and those not available in English. We estimated the total and annual number of published regression-based multivariable CPM development articles, based on the total number of publications, proportion of included articles, and the search sensitivity. Furthermore, we used an adjusted Poisson regression to extrapolate our results to the period 1950-2024. Additionally, we estimated the number of articles that developed CPMs using techniques other than regression (eg, machine learning).</p><p><strong>Results: </strong>From a random sample of 10,660 articles published between 1995 and 2020, 109 regression-based CPM development articles were included. We estimated that 82,772 (95% CI 65,313-100,231) CPM development articles using regression were published, with an acceleration in model development from 2010 onward. With the addition of articles that developed non-regression-based CPMs, the number increased to 147,714 (95% CI 125,201-170,226). After extrapolation to the years 1950-2024, the number of articles increased to 156,673 and 248,431 for regression-based models and total CPMs, respectively.</p><p><strong>Conclusions: </strong>Based on a representative sample of publications from the literature, we estimated that nearly 250,000 articles reporting the development of CPMs across all medical fields were published until 2024. CPM development-related publications continue to increase in number. 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引用次数: 0
摘要
背景:人们对文献中提出的大量新的临床预测模型(cpm)表示担忧。然而,预测研究中这种扩散的程度仍不清楚。目的:本研究旨在估计所有医学领域与CPM发展相关的出版物的总数和年数量。方法:采用经过验证的检索策略,对1995年至2020年底在Pubmed和Embase上发表的预测模型研究文献进行了系统检索。通过每年抽取随机样本,我们确定了符合条件的研究,这些研究开发了一个多变量模型(即诊断或预后),用于在所有医学领域对个人健康结果进行预测。排除标准包括具有单一预测因子的模型开发、不涉及人类的研究、方法学研究、会议摘要、无法获得全文的文章以及没有英文版本的文章。我们根据出版物总数、纳入文章的比例和搜索敏感性,估计了基于回归的多变量CPM发展文章的发表总数和年数量。此外,我们使用调整后的泊松回归将我们的结果外推到1950-2024年。此外,我们估计了使用回归以外的技术(例如机器学习)开发cpm的文章的数量。结果:从1995年至2020年间发表的10,660篇随机样本中,包括109篇基于回归的CPM发展文章。我们估计,使用回归的CPM开发文章发表了82,772篇(95% CI 65,313-100,231),模型开发从2010年开始加速。随着开发非回归cpm的文章的增加,这一数字增加到147,714 (95% CI 125,201-170,226)。外推到1950-2024年,基于回归的模型和总cpm的文章数量分别增加到156,673篇和248,431篇。结论:基于文献中具有代表性的出版物样本,我们估计到2024年,所有医学领域发表了近25万篇报道cpm发展的文章。与CPM发展有关的出版物数量继续增加。为了防止研究浪费和缩小研究与临床实践之间的差距,重点应该从开发新的cpm转移到促进对过多现有cpm的模型验证和影响评估上。本研究的局限性包括搜索仅限于可用的英文文章,以及在人工智能和机器学习模型普及之前开发经过验证的搜索策略。
Number of Publications on New Clinical Prediction Models: A Bibliometric Review.
Background: Concerns have been expressed about the abundance of new clinical prediction models (CPMs) proposed in the literature. However, the extent of this proliferation in prediction research remains unclear.
Objective: This study aimed to estimate the total and annual number of CPM development-related publications available across all medical fields.
Methods: Using a validated search strategy, we conducted a systematic search of literature for prediction model studies published in Pubmed and Embase between 1995 and the end of 2020. By taking random samples for each year, we identified eligible studies that developed a multivariable model (ie, diagnostic or prognostic) for individual-level prediction of a health outcome across all medical fields. Exclusion criteria included development of models with a single predictor, studies not involving humans, methodological studies, conference abstracts, articles with unavailable full text, and those not available in English. We estimated the total and annual number of published regression-based multivariable CPM development articles, based on the total number of publications, proportion of included articles, and the search sensitivity. Furthermore, we used an adjusted Poisson regression to extrapolate our results to the period 1950-2024. Additionally, we estimated the number of articles that developed CPMs using techniques other than regression (eg, machine learning).
Results: From a random sample of 10,660 articles published between 1995 and 2020, 109 regression-based CPM development articles were included. We estimated that 82,772 (95% CI 65,313-100,231) CPM development articles using regression were published, with an acceleration in model development from 2010 onward. With the addition of articles that developed non-regression-based CPMs, the number increased to 147,714 (95% CI 125,201-170,226). After extrapolation to the years 1950-2024, the number of articles increased to 156,673 and 248,431 for regression-based models and total CPMs, respectively.
Conclusions: Based on a representative sample of publications from the literature, we estimated that nearly 250,000 articles reporting the development of CPMs across all medical fields were published until 2024. CPM development-related publications continue to increase in number. To prevent research waste and close the gap between research and clinical practice, focus should shift away from developing new CPMs to facilitating model validation and impact assessment of the plethora of existing CPMs. Limitations of this study include restriction of search to articles available in English and development of the validated search strategy prior to the popularity of artificial intelligence and machine learning models.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.