Associations of Occupational Tasks with Knee and Hip Osteoarthritis: The Johnston County Osteoarthritis Project
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional study examined associations of occupational tasks with radiographic and symptomatic osteoarthritis (OA) in a community-based sample. METHODS: Participants from the Johnston County Osteoarthritis Project (n = 2729) self-reported the frequency of performing 10 specific occupational tasks at the longest job ever held (never/seldom/sometimes vs often/always) and lifetime exposure to jobs that required spending > 50% of their time doing 5 specific tasks or lifting 22, 44, or 110 pounds 10 times weekly. Multivariable logistic regression models examined associations of each occupational task separately with radiographic and symptomatic knee and hip OA, controlling for age, race, gender, body mass index, prior knee or hip injury, and smoking. RESULTS: Radiographic hip and knee OA were not significantly associated with any occupational tasks, but several occupational tasks were associated with increased odds of both symptomatic knee and hip OA: lifting > 10 pounds, crawling, and doing heavy work while standing (OR 1.4-2.1). More occupational walking and standing and less sitting were also associated with symptomatic knee OA, and more bending/twisting/reaching was associated with symptomatic hip OA. Exposure to a greater number of physically demanding occupational tasks at the longest job was associated with greater odds of both symptomatic knee and hip OA. CONCLUSION: Our results confirm an association of physically demanding occupational tasks with both symptomatic knee and hip OA, including several specific activities that increased the odds of OA in both joint groups. These tasks represent possibilities for identifying and targeting at-risk individuals with preventive interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".