Musculoskeletal disorders of the upper extremity associated with computer work: A systematic review
Bibliographic record
Abstract
The epidemiological literature is critically reviewed to evaluate the evidence supporting a causal relationship between computer work and musculoskeletal symptoms and disorders (MSDs) of the hand, wrist, forearm, and elbow. Since 1990, 33 publications were found that met the selection criteria –11 were prospective in design and 22 were cross-sectional. All prospective studies that measured extent of computer use found a positive association between computer work and upper extremity musculoskeletal symptoms. The two prospective studies to conduct physical examinations both found a relationship between hours of computer use and increased risk of upper extremity diagnoses. Of the 22 cross-sectional studies, 17 studies found positive associations. Two of the five studies reporting no association found increased risks of certain disorders under certain combinations of computer use and other factors. The remaining three studies were limited by either a small sample size or low variation of computer use hours within the study population. We conclude that there is consistent evidence of a positive relationship across numerous prospective and cross-sectional studies with increased risk most pronounced beyond 20 hours/week of computer use or with increasing years of computer work. The disorders confirmed with physical examinations are wrist tendonitis and tenosynovitis, medial and lateral epicondylitis, and DeQuervain's tenosynovitis. The risk of carpal tunnel syndrome is increased with use of a computer, especially with mouse use for more than 20 hours per week.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| 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) | 0.006 | 0.001 |
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".