ArticleOccupational medicine (Oxford, England)2025
Generative AI in peer review process for occupational health.
Article in Occupational medicine (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Application of large language models as decision support tools in occupational health and safety management: a cohort study of industrial workers.Frontiers in public health · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundGenerative Artificial Intelligence (AI) tools in academic writing can augment and speed up the proofing process by improving sections of the manuscript. This was the first known instance where the effectiveness and efficiency of Generative AI were quantified.
aimsTo determine the effectiveness and efficiency of these tools in providing feedback and recommendations to the first drafts of eight published occupational health papers.
methodsTo assess effectiveness, manuscripts were reviewed by Microsoft Copilot, ChatGPT (GPT-3.5), Google Gemini 1.0 and five human reviewers in February 2024. Anonymized reviews were scored by two expert panel members using a self-developed structured scoring system. The quality of feedback was rated on its relevance, completeness, accuracy and ability to identify errors and provide constructive feedback. The quality of recommendations was rated on relevance, completeness and accuracy. Efficiency was assessed via the time taken to complete each review. The mean, standard deviation (SD) and level of significance of any differences among the parameters were obtained.
resultsGenerative AI tools were significantly more effective (3.44, SD 0.77, P < 0.001) than human reviewers in providing feedback, while human reviewers performed significantly better (3.36, SD 0.71, P < 0.01) in providing recommendations. Generative AI tools were significantly more time-efficient and had the advantage of being always available. However, time/effort was required to verify the output for fictitious content.
conclusionsThe utilization of Generative AI would improve the speed and accuracy of improving the manuscript prior to publication, leading to greater efficiencies in the dissemination of knowledge to the occupational health community.
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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.