ArticleJournal of the American Medical Informatics Association : JAMIA2025
Establishing best practices in large language model research: an application to repeat prompting.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- A reproducible optimisation protocol for calibrating prompt-based large language model workflows in evidence synthesis.MethodsX · 2026Article
- From Model to Product: Understanding and Teaching AI Evidence.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Comparative evaluation of AI-driven large language models for periodontal-orthodontic patient questions: accuracy, comprehensiveness, readability, and temporal consistency.BMC oral health · 2026Article
- Clinical Plausibility in Large Language Model Robustness Testing for Medicine: A Scoping Review.Journal of medical systems · 2026Article
- Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs.BMJ health & care informatics · 2026Article
- The paradox of creativity in generative AI: high performance, human-like bias, and limited differential evaluation.Frontiers in psychology · 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
objectivesWe aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example. MATERIALS AND
methodsUsing data from a prior study investigating potential model bias in peer review of medical abstracts, we compared methods that ignore correlation in model outputs from repeated prompting with a random effects method that accounts for this correlation.
resultsHigh correlation within groups was found when repeatedly prompting the model, with intraclass correlation coefficient of 0.69. Ignoring the inherent correlation in the data led to over 100-fold inflation of effective sample size. After appropriately accounting for this issue, the authors' results reverse from a small but highly significant finding to no evidence of model bias. DISCUSSION: The establishment of best practices for LLM research is urgently needed, as demonstrated in this case where accounting for repeat prompting in analyses was critical for accurate study conclusions.
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.