ArticleBMC neurology2025
GPT meets PubMed: a novel approach to literature review using a large language model to crowdsource migraine medication reviews.
Article in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the potential of two large language models (LLMs), GPT-4 (OpenAI) and PaLM2 (Google), in automating migraine literature analysis by conducting sentiment analysis of migraine medications in clinical trial abstracts.
backgroundMigraine affects over one billion individuals worldwide, significantly impacting their quality of life. A vast amount of scientific literature on novel migraine therapeutics continues to emerge, but an efficient method by which to perform ongoing analysis and integration of this information poses a challenge.
methods"Sentiment analysis" is a data science technique used to ascertain whether a text has positive, negative, or neutral emotional tone. Migraine medication names were extracted from lists of licensed biological products from the FDA, and relevant abstracts were identified using the MeSH term "migraine disorders" on PubMed and filtered for clinical trials. Standardized prompts were provided to the APIs of both GPT-4 and PaLM2 to request an article sentiment as to the efficacy of each medication found in the abstract text. The resulting sentiment outputs were classified using both a binary and a distribution-based model to determine the efficacy of a given medication.
resultsIn both the binary and distribution-based models, the most favorable migraine medications identified by GPT-4 and PaLM2 aligned with evidence-based guidelines for migraine treatment.
conclusionsLLMs have potential as complementary tools in migraine literature analysis. Despite some inconsistencies in output and methodological limitations, the results highlight the utility of LLMs in enhancing the efficiency of literature review through sentiment analysis.
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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.