Association of Sleep Disturbance With Catastrophizing and Knee Pain: Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between sleep disturbance, catastrophizing, and knee pain in middle-aged and older individuals. METHODS: Data from the Osteoarthritis Initiative cohort from months 48 to 96 were used, where month 48 was treated as baseline. Knee pain (Western Ontario and McMaster Universities Osteoarthritis Index pain scale score ≥5 [range 0-20]), catastrophizing (extracted from Coping Strategies Questionnaire score ≥3 [range 0-6]), and sleep quality (extracted from Center for Epidemiologic Studies Depression Scale [range 1-4]) were assessed annually. We described the association of sleep disturbance with the presence and risk of knee pain and catastrophizing. The mediation effect of knee pain and catastrophizing on the sleep-catastrophizing and sleep-pain association was evaluated, respectively. RESULTS: Catastrophizing and knee pain were reported in 346 (10%) and 917 (24%) of the 3,813 participants (mean 64.9 years, 58% female) at baseline. Participants with worse sleep disturbance were more likely to have knee pain (prevalence ratio [PR] 1.4-2.0, P for trend <0.001) and catastrophizing (PR 1.4-3.1, P for trend <0.001). Sleep disturbance at baseline predicted the risk of knee pain (risk ratio [RR] 1.1, P for trend <0.001) and catastrophizing (RR 1.2-1.7, P for trend <0.001) during follow-up. No statistically significant interactions between sleep disturbance and knee pain or catastrophizing were observed. Knee pain and catastrophizing mediated the sleep-catastrophizing and sleep-pain association, respectively, at baseline, and knee pain negatively mediated the sleep-catastrophizing association longitudinally. CONCLUSION: Sleep disturbance was associated with the presence and risk of catastrophizing and knee pain. Sleep interventions may have a universal and independent effect in preventing incident knee pain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".