ArticleBritish journal of clinical pharmacology2025
Paediatric rare diseases: Can large language models assist off-label prescribing?
Article in British journal of clinical pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Large-language-models for pediatric diagnosis: Performance evaluation using real-world clinical notes from common and rare cases.Pediatric investigation · 2026Article
- Performance of ChatGPT-4o in Providing Information on Pediatric Inborn Errors of Immunity: A Cross-Sectional Evaluation.Journal of clinical medicine · 2026Article
- Off-label prescribing in Italian pediatric practice: insights from a nationwide survey.European journal of pediatrics · 2025Article
- Paediatric rare diseases: Can large language models assist off-label prescribing?British journal of clinical pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
aimsTo evaluate the effectiveness and reliability of large language models (LLMs) in retrieving and synthesizing biomedical information to support off-label drug prescribing in paediatric rare diseases, and to compare their performance with human-authored references in terms of scientific rationale, adverse events and drug interactions.
methodsThe study reviewed 20 cases of off-label prescriptions in rare paediatric diseases using 4 LLMs (i.e., GPT-4o, Sophos-2, Claude-3, Scopus AI). The queries addressed focused on scientific rationale, adverse events and drug interactions. The performance measures encompassed sensitivity, precision, accuracy, F1-score, response quality and reference quality. A Global Performance Score integrated all measures.
resultsAfter evaluating 2758 references and 480 responses, a significant discrepancy was found among 4 LLMs concerning Global Performance Score (P = .001). Posthoc analysis showed that Scopus AI vs. GPT-4o comparison was significant, with GPT-4o showing higher values. Median LLM reference quality often surpassed human performance, yet variability limits conclusions regarding superiority.
conclusionsLLMs are capable of retrieving and synthesizing biomedical information, but performance varies depending on query type and search mode. These tools speed up retrieving relevant information to assess off-label prescribing appropriateness. Despite the promise of artificial intelligence, human oversight remains critical to ensure data accuracy and reliability.
Indexed as
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.