ArticleNature medicine2026
AI-assisted scoping review of code sharing in clinical prediction model research.
Article in Nature medicine, 2026. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Clinical prediction models are increasingly deployed to support diagnostic and prognostic decisions, making reproducibility essential for assessing their reliability and generalizability. Analytical code supports independent assessment of these processes, yet its availability in the literature remains limited. This scoping review quantifies current practices in sharing analytical code to inform the development of TRIPOD-Code, a reporting guideline for code availability and reproducibility. Here a large-language-model-assisted pipeline was developed to screen articles citing TRIPOD or TRIPOD+AI, extract repository links and assess retrieved repositories against 14 predefined reproducibility-related features. Among 3,967 articles, 482 (12.2%) included code-sharing statements. Sharing prevalence varied widely by journal and country. Repository assessment showed substantial heterogeneity in reproducibility features. These findings underscore the need for clearer expectations beyond code availability, including documentation, dependency specification and executable structure. Strengthening these practices may improve the usability of clinical prediction model studies and support their deployment in real-world clinical settings.
Identifiers
42855584What 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.