Relationships and Mechanisms Between Occupational Risk Factors and Distal Upper Extremity Disorders
Bibliographic record
Abstract
OBJECTIVE: The relationships between workplace risk factors and upper extremity injuries from epidemiological and laboratory studies were examined. BACKGROUND: Epidemiological studies are associated with several limitations, affecting the strength of association between risk factors and the development of injuries. METHOD: In this narrative review, we identified epidemiological and laboratory studies (published primarily since 1997) investigating exposure to workplace risk factors (force, repetition, posture, vibration) and risk of hand/wrist tendon-related disorders, epicondylitis, and carpal tunnel syndrome (CTS). RESULTS: Forceful exertions are strongly associated with hand/wrist tendon-related disorders, epicondylitis, and CTS. Dose-response relationships were found for epicondylitis (repetition) and CTS (posture). Interactions demonstrate multiplicative effects of risk factors for injury risk. Laboratory studies display clear associations between task demands and biomechanical measures linked to mechanisms for upper extremity injuries with animal models providing further evidence of a dose-response between risk factors and injury. CONCLUSION: Forceful, repetitive work requiring non-neutral postures are associated with increasing risk of hand/wrist tendon-related disorders, epicondylitis, and CTS as evidenced by epidemiology studies and laboratory-based investigations of humans and animals. APPLICATION: Understanding the relationship between exposure levels of workplace risk factors and upper extremity disorders can improve injury prevention and rehabilitation strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".