Hip pain while using lower extremity joints and sleep disturbances in elderly white women: Results from a cross‐sectional analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate sleep quality in women with hip pain due to daily activities involving the lower extremity joints. METHODS: We evaluated the association of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) hip pain severity score with objective sleep measures obtained by wrist actigraphy in 2,225 white women ≥ 65 years of age enrolled in the Study of Osteoporotic Fractures. RESULTS: Women had an increased odds of spending ≥ 90 minutes awake after sleep onset (odds ratio [OR] 1.28, 95% confidence interval [95% CI] 1.11-1.50) for every 5-point increase in hip pain score after adjustment for all covariates. Hip pain when sitting or lying was the strongest predictor of sleep fragmentation (OR 2.0, 95% CI 1.47-2.73); however, standing pain was associated with a higher number of awake minutes in bed scored from sleep onset to the end of the last sleep episode, independent of pain while in bed (OR 1.41, 95% CI 1.07-2.01). Sleep disturbances increased significantly after the first 2 hours of sleep in women with severe hip pain compared to those without hip pain (mean ± SD 1.4 ± 0.47 minutes per hour of sleep; P < 0.003). Similar associations were observed for long wake episodes >5 minutes. There were no associations between daytime napping, sleep latency, sleep efficiency, and total sleep minutes and WOMAC hip pain. CONCLUSION: Fragmented sleep was greater in women with hip pain compared to those without hip pain; however, fragmented sleep in women with severe hip pain compared to those without hip pain was unchanged until after the first 2 hours of sleep. Further investigations into pain medications wearing off over time or the prolonged periods of inactivity decreasing the pain threshold are warranted.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".