THE MEDIATING ROLE OF PAIN SEVERITY IN THE ASSOCIATION BETWEEN DEPRESSIVE SYMPTOMS AND GAIT SPEED
Bibliographic record
Abstract
Abstract Knee osteoarthritis (OA) and comorbid depression adversely impact gait speed over time. Depression can alter pain tolerance and perception; therefore, self-reported knee pain severity may mediate the relationship between depressive symptoms and gait speed, but this has not been evaluated longitudinally using repeated measures data. Thus, we assessed whether knee pain severity mediates the association between depressive symptoms and gait speed over time. Participants (n=2,222) from the Osteoarthritis Initiative with radiographic knee OA (Kellgren-Lawrence grade ≥ 2) in at least one knee were included. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D; range 0-60) at baseline and first two annual follow-up visits. The twenty-meter gait speed (meters per second) outcome was assessed at the second through fourth annual follow-up visits. Knee pain severity was assessed as a mediator and measured using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain subscale at the first through third annual follow-up visits. Linear regression mediation using a version of the Sobel-Goodman approach was the primary method of analysis. Indirect and direct effects of depressive symptoms on gait speed for each one-unit CES-D score were -0.001 (p < 0.001) and -0.003 (p = 0.020) standard deviations, respectively. Thus, approximately 24% of the association between depressive symptoms and gait speed was mediated by knee pain severity across time. The current results support the need for simultaneous, multidisciplinary interventions for both depressive symptoms and knee OA pain to prevent future decline in physical performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".