Associations between work-related factors and specific disorders at the elbow: a systematic literature review
Bibliographic record
Abstract
OBJECTIVES: To assess the exposure-response relationships between work-related physical and psychosocial factors and lateral epicondylitis, medial epicondylitis, cubital tunnel syndrome and radial tunnel syndrome in occupational populations. METHODS: A systematic review of the literature was conducted on the associations between type of work, physical load and psychosocial aspects at work and the occurrence of specific elbow disorders. Associations between work factors and these elbow disorders were expressed in quantitative measures: odds ratio (OR) or relative risk (RR). RESULTS: Handling tools >1 kg (ORs of 2.1-3.0), handling loads >20 kg at least 10 times/day (OR 2.6) and repetitive movements >2 h/day (ORs of 2.8-4.7) were associated with lateral epicondylitis. Psychosocial factors associated with lateral epicondylitis were low job control (OR 2.2) and low social support (OR 1.8). Handling loads >5 kg (2 times/min at minimum of 2 h/day), handling loads >20 kg at least 10 times/day, high hand grip forces for >1 h/day, repetitive movements for >2 h/day (ORs of 2.2-3.6) and working with vibrating tools >2 h/day (OR 2.2) were associated with medial epicondylitis. The occurrence of cubital tunnel syndrome was associated with the factor 'holding a tool in position' (OR 3.53). Handling loads >1 kg (OR 9.0; 95% CI 1.4, 56.9), static work of the hand during the majority of the cycle time (OR 5.9) and full extension (0-45 degrees ) of the elbow (OR 4.9) were associated with radial tunnel syndrome. CONCLUSIONS: Several physical and psychosocial factors at work may result in an increased occurrence of specific disorders at the elbow.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".