ArticleScientific reports2026
Multidisciplinary expert evaluation of large language models on questions regarding bariatric surgery: a comparative analysis of ERNIE Bot 4.0, ChatGPT-4, Claude 3 Opus, and Gemini Pro.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A multimodal deep learning framework for clinical nursing assessment in lumbar fusion surgery via representation learning and feature extraction.Scientific reports · 2026Article
- 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
10 authors.
Funding
Abstract
Large language models (LLMs) have potential in bariatric surgery consultations, but current evaluations are limited to bariatric specialists, contradicting guidelines that call for multidisciplinary assessment. This study uses a multidisciplinary framework to evaluate LLM performance on bariatric surgery queries. Four LLMs (ERNIE Bot 4.0, ChatGPT-4, Claude 3 Opus, and Gemini Pro) were tested on 50 common bariatric surgery questions, generating 200 responses. A panel of seven experts (4 bariatric surgeons, 1 obesity physician, and 2 dietitians) assessed accuracy and comprehensiveness. Three prompt approaches were used to evaluate self-correction: basic review, web-enabled review, and evidence-based review. Qualitative analysis identified poorly rated responses. The study adheres to TRIPOD-LLM Statement reporting guideline. Rater agreement was fair, accuracy rating, Fleiss’ kappa = 0.210 (95% CI: 0.208–0.212; Z = 7.815; P < 0.001); comprehensiveness rating, Fleiss’ kappa = 0.464 (95% CI: 0.453–0.476; Z = 2.543; P < 0.011). Claude 3 Opus provided the longest answers, while ERNIE Bot 4.0 had the highest accuracy (19.46 ± 2.07). ChatGPT-4 had 90.0% “good” responses, compared to 84.0% for ERNIE Bot 4.0, 80.0% for Claude 3 Opus, and 48.0% for Gemini Pro (P ≤ 0.05). All LLMs performed sub-optimally in comprehensiveness (scores: 2.86–3.13 out of 5). However, they showed significant self-correction capabilities, especially when using internet searches and evidence-based resources. LLMs show potential for bariatric surgery education, but their direct clinical use is challenging due to variable accuracy and suboptimal comprehensiveness. Future efforts should focus on developing specialized LLMs with robust evidence and multidisciplinary input to ensure patient safety and optimal outcomes.
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.