ArticleRevista da Associacao Medica Brasileira (1992)2026
Evaluating chat generative pre-trained transformer responses to common patient questions on temporomandibular disorders.
Article in Revista da Associacao Medica Brasileira (1992), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTemporomandibular disorders are among the most common causes of orofacial pain, often leading patients to seek information online. The increasing use of large language models such as chat generative pre-trained transformer in healthcare communication has raised questions about the reliability and readability of artificial intelligence-generated patient information. The aim of this study was to evaluate the accuracy, comprehensiveness, readability, and inter-rater reliability of chat generative pre-trained transformer-generated responses to common patient questions regarding temporomandibular disorders.
methodsChatGPT (version 4.0) was prompted to generate 50 potential patient questions about temporomandibular disorders. Ten representative questions were selected and independently evaluated by five experts (two oral and maxillofacial surgeons, two physiotherapists, and one physical medicine specialist). Responses were rated using a four-point quality scale assessing accuracy and completeness. Readability was calculated using the Flesch-Kincaid method, and inter-rater reliability was assessed using the Intraclass Correlation Coefficient.
resultsThe responses demonstrated variable but generally acceptable quality. The overall Intraclass Correlation Coefficient value was 0.862, indicating good inter-rater agreement. Readability levels ranged from grade 6.2-10.7 (mean 8.0), corresponding to middle-to-high school comprehension. While most responses were rated satisfactory, several lacked sufficient clinical detail, particularly in differentiating professional consultation pathways.
conclusionchat generative pre-trained transformer provides moderately reliable and readable information about temporomandibular disorders, supporting its potential role in patient education. However, reliance on artificial intelligence-generated frequently asked questions introduces methodological limitations and authority bias. Future studies should incorporate real patient data and external fact-checking to enhance clinical relevance.
Indexed as
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.