ArticleCureus2024
The Impact of Temperature on Extracting Information From Clinical Trial Publications Using Large Language Models.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Tools for dementia disclosure: a systematic review of -family caregivers' perspectives and experiences.The Gerontologist · 2026Pooled it
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- OphthoChat: A HIPAA-Compliant, Artificial Intelligence-Driven Natural-Language Chatbot for Ophthalmic Electronic Medical Record Querying and Analysis.Ophthalmology science · 2026Article
- The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- A new AI assisted approach aligns data standards and accelerates interoperability in biomedical research.NPJ digital medicine · 2026Article
- Reproducibility and Robustness of Large Language Models for Mobility Functional Status Extraction.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- Large Language Models for Supporting Clear Writing and Detecting Spin in Randomized Controlled Trials in Oncology: Comparative Analysis of GPT Models and Prompts.JMIR cancer · 2026Article
- Comparative performance of four large language models in generating evidence-based exercise prescriptions using FITT-VP framework.Frontiers in physiology · 2026Article
- A multi-agent approach to neurological clinical reasoning.PLOS digital health · 2025Article
- Piloting Temperature-Driven Variability in Emergency Diagnostic Accuracy Using a Leading Large Language Model.Cureus · 2025Article
- Enhancing Patient Education with AI: A Readability Analysis of AI-Generated Versus American Academy of Ophthalmology Online Patient Education Materials.Journal of clinical medicine · 2025Article
- Data extraction from free-text stroke CT reports using GPT-4o and Llama-3.3-70B: the impact of annotation guidelines.European radiology experimental · 2025Article
- Using large language models for extracting stressful life events to assess their impact on preventive colon cancer screening adherence.BMC public health · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction The application of natural language processing (NLP) for extracting data from biomedical research has gained momentum with the advent of large language models (LLMs). However, the effect of different LLM parameters, such as temperature settings, on biomedical text mining remains underexplored and a consensus on what settings can be considered "safe" is missing. This study evaluates the impact of temperature settings on LLM performance for a named entity recognition and a classification task in clinical trial publications. Methods Two datasets were analyzed using GPT-4o and GPT-4o-mini models at nine different temperature settings (0.00-2.00). The models were used to extract the number of randomized participants and classify abstracts as randomized controlled trials (RCTs) and/or as oncology-related. Different performance metrics were calculated for each temperature setting and task. Results Both models provided correctly formatted predictions for more than 98.7% of abstracts across temperatures from 0.00 to 1.50. While the number of correctly formatted predictions started to decrease afterward with the most notable drop between temperatures 1.75 and 2.00, the other performance metrics remained largely stable. Conclusion Temperature settings at or below 1.50 yielded consistent performance across text-mining tasks, with performance declines at higher settings. These findings are aligned with research on different temperature settings for other tasks, suggesting stable performance within a controlled temperature range across various NLP applications.
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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.