ArticleFrontiers in public health2026
Determinants of work-related musculoskeletal disorders among coal miners in Jining, China: development of a predictive risk model.
Article in Frontiers in public health, 2026. 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.
- Machine learning analysis of the association between psychosocial risks and burnout syndrome in mining workers.Frontiers in public 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Work-related musculoskeletal disorders (WMSD) are highly prevalent among coal miners and pose a significant threat to occupational health. Understanding the underlying risk factors and developing a predictive model for WMSD risk can help to mitigate WMSD. Objective: To identify key determinants of WMSD among coal miners in Jinang, China, and construct a predictive model to assess risk. Methods: One thousand four hundred nine coal miners from two coal mining companies were surveyed using the modified Chinese Muscle Questionnaire (CMQ). Prevalence rates and risk factors were assessed using logistic regression. Machine learning algorithms were applied to construct the predictive model. Results: The 12-month overall prevalence of WMSD was 82%, with the neck (59.5%), shoulders (53.4%), and lower back (46.5%) being the most affected. Eight variables, including smoking behaviors, perceived health status, and uncomfortable working posture, were significantly associated with WMSD ( Conclusion: Work-related musculoskeletal disorders are highly prevalent among Chinese coal miners and are influenced by personal and work-related factors. Machine learning models, particularly ensemble approaches, offer promise for risk prediction and targeted prevention.
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.