Primary osteoarthritis of hip, knee, and hand in relation to occupational exposure
Bibliographic record
Abstract
AIM: To identify occupations with excess prevalence of osteoarthritis of the knee, hip, and hand in a nationwide survey and to compare occupations with and without excess prevalence with regard to biomechanical stresses and severity of osteoarthritis. METHODS: Patients presenting with osteoarthritis of the knee, hip, or hand were recruited throughout France by their treating physician who collected information on history, including age at onset, occupation, and occupational stresses to joints. Severity was assessed using joint specific functional status questionnaires: Lequesne for the hip and knee and Dreiser for the hand. The distribution of osteoarthritis patients by occupation was compared with the distribution of occupations in all workers in France to obtain prevalence rate ratios. RESULTS: Occupations with the greatest prevalence rate ratio were female cleaners (6.2; 95% CI 4.6 to 8.0), women in the clothing industry (5.0; 95% CI 3.9 to 6.3), male masons and other construction workers (2.9; 95% CI 2.6 to 3.3), and agriculture male and female workers (2.8; 95% CI 2.5 to 3.2). A twofold greater prevalence rate was observed within certain occupations between self-employed and salaried workers. Early onset of osteoarthritis was seen in the more heavy labour jobs with almost 40% of patients reporting their first symptoms before the age of 50. CONCLUSION: The early onset and severity of osteoarthritis in certain occupations warrants an urgent need for occupation specific studies for the development and evaluation of preventive strategies in this leading cause of disability in Western countries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".