Occupational Risk in Knee Osteoarthritis: A Systematic Review and Meta‐Analysis of Observational Studies
Bibliographic record
Abstract
OBJECTIVE: To assess the association between occupational exposures and knee osteoarthritis (OA). METHODS: We systematically searched for observational studies that examined the relationship between occupational exposures and knee OA and total knee replacement. Four databases were searched up to October 1, 2019. Two reviewers independently assessed study quality using the Newcastle-Ottawa Scale and evidence quality using the Grading of Recommendations Assessment, Development and Evaluation approach. Subgroup meta-analyses were conducted for important study characteristics and each type of occupational exposure. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated for the meta-analysis using random-effects models. RESULTS: Eighty eligible studies were identified including 25 case-control (n = 20,505 total participants), 36 cross-sectional (n = 139,463 total participants), and 19 cohort studies (n = 16,824,492 total participants). A synthesis of 71 studies suggested increased odds of knee OA (OR 1.52 [95% CI 1.37-1.69]) by combining different physically demanding jobs and occupational activities as compared to sedentary occupations and/or low-exposure groups. Odds of knee OA were greater in males and in industry-based studies and studies assessing lifetime occupational exposures. There were 9 specific job titles that were associated with knee OA, including farmer, builder, metal worker, and floor layer. Occupational lifting, kneeling, climbing, squatting, and standing were all associated with higher odds of knee OA as compared to the odds of knee OA in sedentary workers. CONCLUSION: Strenuous, physically demanding occupations and occupational activities were associated with increased odds of knee OA as supported by moderate-quality evidence. Specifically, agricultural and construction sectors, which typically involve heavy lifting, frequent climbing, prolonged kneeling, squatting, and standing, carried increased odds of knee OA.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".