Association Between Cumulative Joint Loading From Occupational Activities and Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the associations between cumulative occupational physical load (COPL) and 3 definitions of knee osteoarthritis (OA). METHODS: Cross-sectional analyses were performed from 2 population-based cohorts (n = 327). Eligible symptomatic participants were those with pain, aching, or discomfort in or around the knee on most days of a month at any time in the past and any pain in the past 12 months. Asymptomatic participants responded "no" to both knee pain questions. Self-reported COPL was calculated over each participant's lifetime and then categorized into quarters (QCOPL). Radiographic OA (ROA) and symptomatic OA (SOA) were defined by Kellgren/Lawrence grade ≥2, with SOA also including pain. Magnetic resonance imaging (MRI) OA was defined using criteria by Hunter et al. Logistic regression, adjusted with population weights, examined the associations between QCOPL and each of ROA, SOA, and MRI-OA after controlling for covariates and two-way interactions. RESULTS: Participants had a mean ± SD age of 58.5 ± 11.0 years and a mean ± SD body mass index of 26.3 ± 4.7 kg/m(2) . Of those, 109 (33.3%) had ROA, 102 (31.2%) had SOA, and 131 (40.1%) had MRI-OA. Compared with QCOPL-1, increased odds of ROA were found for QCOPL-4 (odds ratio [OR] 3.15, 95% confidence interval [95% CI] 1.02-9.70) and QCOPL-3 (OR 4.19, 95% CI 1.55-11.34). Statistically significant relationships were found in SOA (QCOPL-4: OR 8.16, 95% CI 1.89-35.27; QCOPL-3: OR 5.73, 95% CI 1.36-24.12) and MRI-OA (QCOPL-4: OR 9.54, 95% CI 2.65-34.27; QCOPL-3: OR 9.04, 95% CI 2.65-30.88; QCOPL-2: OR 7.18, 95% CI 2.17-23.70). CONCLUSION: Occupational activity is associated with knee OA, with dose-response relationships observed in SOA and MRI-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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".