ArticleBMC oral health2025
AI-driven abstract generating: evaluating LLMs with a tailored prompt under the PRISMA-A framework.
Article in BMC oral health, 2025. 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.
- Comparative evaluation of large language models for guideline-compliant abstract generation and readability in dental research: an experimental comparative study.Scientific reports · 2026Article
- Performance of large language models and prompt engineering strategies for data extraction in systematic reviews.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to assess and compare ChatGPT-4o and Gemini Pro's ability to generate structured abstracts from full-text systematic reviews and meta-analyses in orthodontics, based on adherence to the PRISMA Abstract (PRISMA-A) Checklist, using a customised prompt developed for this purpose. MATERIALS AND
methodsA total of 162 full-text systematic reviews and meta-analyses published in Q1-ranked orthodontic journals since January 2019 were included. Each full-text article was processed by ChatGPT-4o and Gemini Pro, using a PRISMA-A Checklist-aligned structured prompt. Outputs were scored using a tailored Overall quality Score OQS derived from 11 PRISMA-A checklist. Inter-rater and time-dependent reliability were assessed with Intraclass Correlation Coefficients (ICCs), and model outputs were compared using Mann-Whitney U tests.
resultsBoth models yielded satisfactory OQS in generating PRISMA-A checklist compliant abstracts; however, ChatGPT-4o consistently achieved higher scores than Gemini Pro. The most notable differences were observed in the "Included Studies" and "Synthesis of Results" sections, where ChatGPT-4o produced more complete and structurally coherent outputs. ChatGPT-4o achieved a mean OQS of 21.67 (SD 0.58) versus 21.00 (SD 0.71) for Gemini Pro, a difference that was highly significant (p < 0.001).
conclusionsBoth LLMs demonstrated the ability to generate PRISMA-A-compliant abstracts from systematic reviews, with ChatGPT-4o consistently achieving higher quality scores than Gemini Pro. While tested in orthodontics, the approach holds potential for broader applications across evidence-based dental and medical research. Systematic reviews and meta-analyses are essential to evidence-based dentistry but can be challenging and time-consuming to report in accordance with established standards. The structured prompt developed in this study may assist researchers in generating PRISMA-A-compliant outputs more efficiently, helping to accelerate the completion and standardisation of high-level clinical evidence reporting.
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.