Dynamic Effects of Depressive Symptoms on Osteoarthritis Knee Pain
Bibliographic record
Abstract
OBJECTIVE: To estimate the dynamic causal effects of depressive symptoms on osteoarthritis (OA) knee pain. METHODS: Marginal structural models were used to examine dynamic associations between depressive symptoms and pain over 48 months among older adults (n = 2,287) with radiographic knee OA (Kellgren/Lawrence grade 2 or 3) in the Osteoarthritis Initiative. Depressive symptoms at each annual visit were assessed (threshold ≥16) using the Center for Epidemiologic Studies Depression Scale. OA knee pain was measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale, rescaled to range from 0 to 100. RESULTS: Depressive symptoms at each visit were generally not associated with greater OA knee pain at subsequent time points. Causal mean differences in WOMAC pain score comparing depressed to nondepressed patients ranged from 1.78 (95% confidence interval [95% CI] -0.73, 4.30) to 2.58 (95% CI 0.23, 4.93) within the first and fourth years, and the depressive symptoms by time interaction were not statistically significant (P = 0.94). However, there was a statistically significant dose-response relationship between the persistence of depressive symptoms and OA knee pain severity (P = 0.002). Causal mean differences in WOMAC pain score comparing depressed to nondepressed patients were 0.89 (95% CI -0.17, 1.96) for 1 visit with depressive symptoms, 2.35 (95% CI 0.64, 4.06) for 2 visits with depressive symptoms, and 3.57 (95% CI 0.43, 6.71) for 3 visits with depressive symptoms. CONCLUSION: The causal effect of depressive symptoms on OA knee pain does not change over time, but pain severity significantly increases with the persistence of depressed mood.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".