A longitudinal study to explain the pain‐depression link in older adults with osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether osteoarthritis (OA) pain determines depressed mood, taking into consideration fatigue and disability and controlling for other factors. METHODS: In a community cohort with hip/knee OA, telephone interviews assessed OA pain and disability (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC]), fatigue (Multidimensional Fatigue Symptom Inventory), depressed mood (Center for Epidemiologic Studies Depression Scale), and covariates (demographics, self-rated health, comorbidity, pain coping, pain catastrophizing, and social support) at 3 time points over 2 years. Drawing on previous research, a path model was developed to test the interrelationships among the key concepts (pain, depression, fatigue, disability) over time, controlling for covariates. RESULTS: The baseline mean age was 75.4 years; 78.5% of the subjects were women, 37.2% were living alone, and 15.5% had ≥3 comorbid conditions. WOMAC scores indicated moderate OA symptoms and disability. From the final model with 529 subjects, adjusting for covariates, we found that current OA pain strongly predicted future fatigue and disability (both short and long term), that fatigue and disability in turn predicted future depressed mood, that depressed mood and fatigue were interrelated such that depressed mood exacerbated fatigue and vice versa, and that fatigue and disability, but not depressed mood, led to worsening of OA pain. CONCLUSION: Controlling for other factors, OA pain determined subsequent depressed mood through its effect on fatigue and disability. These effects led to worsening of pain and disability over time. These results support the need for improved pain management in OA to prevent or attenuate the downstream effects of pain on disability and 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".