ArticlePloS one2026
Trial Files: Leveraging large language models to summarize practice-changing clinical trials for clinicians.
Article in PloS one, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEach day, over 100 randomized controlled trials (RCTs) are published, making it impossible for clinicians to stay up-to-date with medical literature. Large language models (LLMs) can identify and summarize emerging clinical evidence and support medical education.
methodsWe created and prospectively evaluated a newsletter, Trial Files, which leverages an LLM to summarize RCT abstracts relevant to general internal medicine. We created a software tool, called PaperScrape, which leverages the Medline application programming interface (API) to identify trials published in five high-impact journals. Information from each RCT's abstract was extracted, and plain-language summaries were generated using OpenAI's LLM API. We analyzed the accuracy of summaries generated by an LLM (compared to manual review), results of a subscriber survey, and effectiveness of marketing strategies on user growth.
resultsFrom June 2023 to March 2025, 50 newsletters with 3 RCTs each were distributed to 648 subscribers. A subset of 96 RCTs was randomly selected to evaluate reporting accuracy with prompt engineering. The accuracy for reporting study information with prompt engineering, compared to manual review, was 97.1% for study phase, 92.2% for blinding, 85.4% for sample size, 97.9% for patient population, 94.7% for comparison groups, and 92.7% for primary outcome. Forty-three subscribers completed a survey about Trial Files. The mean overall rating was 4.7 out of 5 (5 representing "very good"), and all respondents agreed the newsletter made it easier to keep up-to-date with emerging clinical trials in internal medicine. The most effective strategy for user growth was promotion at a meeting, conference, or education session (6.8 subscribers per day, compared to 0.7 subscribers gained per day on days without promotion, p < 0.0001).
conclusionLLMs can provide concise, accurate summaries of RCTs, which can help general internists stay up-to-date on recently published trials.
Indexed as
Identifiers
What Socratic 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 Socratic graph.