Sleep Quality Is Related to Worsening Knee Pain in Those with Widespread Pain: The Multicenter Osteoarthritis Study
Bibliographic record
Abstract
OBJECTIVE: We examined the association between sleep and odds of developing knee pain, and whether this relationship varied by status of widespread pain (WSP). METHODS: At the 60-month visit of the Multicenter Osteoarthritis Study, sleep quality and restless sleep were each assessed by using a single item from 2 validated questionnaires. Each sleep measure was categorized into 3 levels, with poor/most restless sleep as the reference. WSP was defined as pain above and below the waist on both sides of the body and axially using a standard homunculus, based on the American College of Rheumatology criteria. Outcomes from 60-84 months included (1) knee pain worsening (KPW; defined as minimal clinically important difference in WOMAC pain), (2) prevalent, and (3) incident consistent frequent knee pain. We applied generalized estimating equations in multivariable logistic regression models. RESULTS: We studied 2329 participants (4658 knees; 67.9 yrs, body mass index 30.9]. We found that WSP modified the relationship between sleep quality and KPW (p = 0.002 for interaction). Among persons with WSP, OR (95% CI) for KPW was 0.53 (0.35-0.78) for those with very good sleep quality (p trend < 0.001); additionally, we found the strongest association of sleep quality in persons with > 8 painful joint sites (p trend < 0.01), but not in those with ≤ 2 painful joint sites. Similar results were observed using restless sleep, in the presence of WSP. The cross-sectional relationship between sleep and prevalence of consistent frequent knee pain was significant. CONCLUSION: Better sleep was related to less KPW with coexisting widespread 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".