Proportion and Number of Upper-Extremity Musculoskeletal Disorders Attributable to the Combined Effect of Biomechanical and Psychosocial Risk Factors in a Working Population
Bibliographic record
Abstract
The objective of this paper is to assess the combined effect of occupational biomechanical and psychosocial risk factors on the incidence of work-related upper-extremity musculoskeletal disorders (UEMSDs) and estimate the proportion and number of incident cases attributable to these risk factors in a working population. Using data from the French COSALI (COhorte des SAlariés LIgériens) cohort (enrolment phase: 2002-2005; follow-up phase: 2007-2010), a complete case analysis including 1246 workers (59% men, mean age: 38 years ± 8.6 at baseline) was performed. All participants underwent a standardized clinical examination at enrolment and 1611 workers were re-examined at follow-up. Population attributable fractions and the number of UEMSD cases attributable to occupational risk factors were calculated. During follow-up, 139 UEMSD cases were diagnosed, representing an estimated 129,320 projected incident UEMSD cases in the working population. After adjusting for personal factors, in model 1, 8664 cases (6.7%) were attributable to low social support, 19,010 (14.7%) to high physical exertion, and 20,443 (15.8%) to co-exposure to both factors. In model 2, 16,294 (12.6%) cases were attributable to low social support, 6983 (5.4%) to posture with arms above shoulder level, and 5043 (3.9%) to co-exposure to both factors. Our findings suggest that many cases of UEMSD could be potentially prevented by multidimensional interventions aimed at reducing exposure to high physical exertion and improving social support at work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".