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关于新临床预测模型的出版物数量:一项文献计量学综述。

Number of Publications on New Clinical Prediction Models: A Bibliometric Review.

作者信息

Arshi Banafsheh, Wynants Laure, Rijnhart Eline, Reeve Kelly, Cowley Laura Elizabeth, Smits Luc J

机构信息

Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Peter Debyeplein 1, P.O. Box 616, Maastricht, 6200 MD, The Netherlands, 31 433882821.

Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands.

出版信息

JMIR Med Inform. 2025 Jul 4;13:e62710. doi: 10.2196/62710.

Abstract

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.

摘要

背景

文献中提出的新临床预测模型(CPM)数量众多,引发了人们的关注。然而,预测研究中这种模型激增的程度仍不明确。

目的

本研究旨在估计所有医学领域中与CPM开发相关的出版物总数及年度数量。

方法

我们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系统检索。通过对每年随机抽样,我们确定了符合条件的研究,这些研究开发了用于所有医学领域个体健康结局预测的多变量模型(即诊断或预后模型)。排除标准包括仅含单一预测因素的模型开发、不涉及人类的研究、方法学研究、会议摘要、无全文的文章以及非英文文章。我们根据出版物总数、纳入文章比例和检索灵敏度,估计了已发表的基于回归的多变量CPM开发文章的总数及年度数量。此外,我们使用调整后的泊松回归将结果外推至1950 - 2024年期间。另外,我们还估计了使用回归以外技术(如机器学习)开发CPM的文章数量。

结果

从1995年至2020年发表的10660篇文章的随机样本中,纳入了109篇基于回归的CPM开发文章。我们估计,使用回归方法的CPM开发文章有82772篇(95%置信区间65313 - 100231),且从2010年起模型开发加速。加上开发非基于回归的CPM的文章,数量增至147714篇(95%置信区间125201 - 170226)。外推至1950 - 2024年,基于回归的模型和CPM总数的文章数量分别增至156673篇和248431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,在所有医学领域中,近250000篇报告CPM开发的文章已发表。与CPM开发相关的出版物数量持续增加。为防止研究浪费并缩小研究与临床实践之间的差距,应将重点从开发新的CPM转向促进对大量现有CPM的模型验证和影响评估。本研究的局限性包括检索仅限于英文文章,以及在人工智能和机器学习模型流行之前开发经过验证的检索策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3de/12252138/bef5bfd0c0db/medinform-v13-e62710-g001.jpg

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