SynthesisJournal of evidence-based medicine2025
From Manual to Machine: Revolutionizing Day Surgery Guideline and Consensus Quality Assessment With Large Language Models.
Synthesis in Journal of evidence-based medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improvement of Clinical Practice Guideline Appraisal by Human Experts and AI Agents by Using Structured Guidance: Systematic Review, Meta-Analysis, and Validation Study.Journal of medical Internet research · 2026Pooled it
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- Parental readiness for discharge in day surgery children: current status and associated factors.Translational pediatrics · 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
10 authors.
Funding
Abstract
objectiveTo evaluate the methodological and reporting quality of clinical practice guidelines/expert consensus for ambulatory surgery centers published since 2000, combining manual assessment with large language model (LLM) analysis, while exploring LLMs' feasibility in quality evaluation.
methodsWe systematically searched Chinese/English databases and guideline repositories. Two researchers independently screened literature and extracted data. Quality assessments were conducted using AGREE II and RIGHT tools through both manual evaluation and GPT-4o modeling.
results54 eligible documents were included. AGREE II domains showed mean compliance: Scope and purpose 25.00%, Stakeholder involvement 20.16%, Rigor of development 17.28%, Clarity of presentation 41.56%, Applicability 18.06%, Editorial independence 26.39%. RIGHT items averaged: Basic information 44.44%, Background 36.11%, Evidence 14.07%, Recommendations 34.66%, Review and quality assurance 3.70%, Funding and declaration and management of interests 24.54%, Other information 27.16%. LLMs'-evaluated documents demonstrated significantly higher scores than manual assessments in both tools. Subgroup analyses revealed superior quality in documents with evidence retrieval, conflict disclosure, funding support, and LLM integration (P <0.05).
conclusionCurrent guidelines and consensus related to day surgery need to improve their methodological quality and quality of reporting. The study validates LLMs' supplementary value in quality assessment while emphasizing the necessity of maintaining manual evaluation as the foundation.
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.