WOMAC score and arthritis diagnosis predict decreased agricultural productivity
Bibliographic record
Abstract
BACKGROUND: Arthritis and joint pain are highly prevalent in agricultural (ag) workers. Many ag operations are sustained by a small number of workers, and the disability of even one worker thus contributes to economic hardship. This study investigated associations between joint health in Montana ag workers and economic well-being and work capacity. METHODS: This observational mixed-methods study utilized quantitative survey data and qualitative focus group data. 299 ranchers and farmers in 9 Montana counties completed either an online or paper survey that included participant demographics, joint symptoms, history of arthritis and arthritis type, financial status, work capacity, and the need to rely on others to complete one's work. The Western Ontario and McMaster Universities arthritis index (WOMAC) survey was completed by those with hip or knee pain. Data were entered into REDCap v8.9.2 for analysis with SAS 9.4, using logistic and linear regression models to detect associations between covariables and to calculate odds ratios and confidence intervals. Focus groups were held with ranchers in two Montana counties, discussing similar topics, and the themes expressed were identified. RESULTS: 87.6% of survey respondents reported joint pain, 47.8% a diagnosis of arthritis, and 22.4% osteoarthritis (OA). A 10-point increase in WOMAC was significantly associated with lower work capacity (OR 2.00; 95% CI [1.58, 2.55], p < 0.01), worse financial condition (OR 1.23; 95% CI [1.01,1.48], p = 0.04), and increased reliance on others (OR 1.82; 95% CI [1.32, 2.55], p < 0.01). An arthritis diagnosis was associated with worsening work capacity (OR 4.66; 95% CI [2.71, 8.01], p < 0.01) and increased odds of relying on others (OR 3.23; 95% CI [1.56, 6.66], p < 0.01). A diagnosis of OA was significantly associated with decreased work capacity (OR 3.47; 95% CI [1.97, 6.11], p < 0.01). Unadjusted for age and BMI, we found a significant association between years spent working in agriculture and joint health, which became non-significant after adjusting for age and BMI. Focus group themes included decreased productivity with increased joint symptoms and a tendency for ranchers to avoid interaction with the health care system. CONCLUSION: Poor joint health is associated with economic risk on Montana ranches and farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".