ArticleNPJ digital medicine2025
Language models for data extraction and risk of bias assessment in complementary medicine.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Integration of large language models and evidence-based Chinese medicine: A scoping review.Integrative medicine research · 2026Review
- Clinical evidence on integrated Chinese-Western medicine for primary dysmenorrhea.Integrative medicine research · 2026Article
- Automated data extraction for systematic reviews using GPT-5.2 and Google Gemini Pro 3: A dual-large language model approach in orthopaedic research.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- From API to Action: A Multi-Model Comparison of OpenAI, Anthropic, Google, and Meta LLMs for Clinical Trial Data Extraction.Bioengineering (Basel, Switzerland) · 2026Article
- Using Generative AI to Appraise the Quality of Medical Education Research Studies: Agreement Between AI-Generated and Human MERSQI Scores.AEM education and training · 2026Article
- Large language models in systematic review and meta-analysis of surgical treatments for vaginal vault prolapse.NPJ digital medicine · 2026Article
- How large language models can help us write a systematic review.Intensive care medicine · 2026Article
- Human-AI collaboration enhances the performance of large language models in risk of bias assessment.BMC medical research methodology · 2026Article
- Construction and evaluation of the knowledge graph and large model question-answering system for Jin San Zhen therapy: a tool study for primary care and general practice.Frontiers in medicine · 2026Article
- Performance of large language models and prompt engineering strategies for data extraction in systematic reviews.Frontiers in digital health · 2026Article
- Evaluating large language model performance in Risk of Bias assessments: A cross-sectional validation study.PloS one · 2026Article
- WHO global research priorities for traditional, complementary, and integrative (TCI) medicine: an international consensus and comparisons with LLMs.Journal of global health · 2025Article
- Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.World journal of gastrointestinal oncology · 2025Review
- Automated analyses of risk of bias and critical appraisal of systematic reviews (ROBIS and AMSTAR 2): a comparison of the performance of 4 large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Review
- Large Language Model–Assisted Risk-of-Bias Assessment in Randomized Controlled Trials Using the Revised Risk-of-Bias Tool: Evaluation Study.Journal of medical Internet research · 2025Article
- Risk of Bias Assessment of Diagnostic Accuracy Studies Using QUADAS 2 by Large Language Models.Diagnostics (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have the potential to enhance evidence synthesis efficiency and accuracy. This study assessed LLM-only and LLM-assisted methods in data extraction and risk of bias assessment for 107 trials on complementary medicine. Moonshot-v1-128k and Claude-3.5-sonnet achieved high accuracy (≥95%), with LLM-assisted methods performing better (≥97%). LLM-assisted methods significantly reduced processing time (14.7 and 5.9 min vs. 86.9 and 10.4 min for conventional methods). These findings highlight LLMs' potential when integrated with human expertise.
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.