ArticlemedRxiv : the preprint server for health sciences2025
Epidemiologic Method Review at Scale: Assessing Charlson Comorbidity Versioning Using a Large Language Model.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The Charlson Comorbidity Index (CCI) is widely used in epidemiologic studies. However, many versions of the CCI have been developed since the original method was published in 1987, and it is unclear which version is used most frequently and how version utilization in research has changed over time. We present an approach using a large language model to extract data from articles by detecting which specific CCI version is employed. We analyzed 31,767 articles published since 2012 to evaluate the landscape of CCI implementation. We show that 63% of articles that cite a single method version cite only the original CCI publication, which cannot be applied in the modern real-world data era, leading to ambiguity in actual CCI implementation. More generally, this paper introduces a generalizable approach to scaling methods literature review beyond typical human-review, which we validate and demonstrate through application to the CCI.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.