Insomnia symptoms among individuals with osteoarthritis and symptoms indicative of osteoarthritis: A population-based cross-sectional study using the CLSA
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence and examine the likelihood of insomnia symptoms in middle- and older-aged adults with an osteoarthritis (OA) diagnosis and with joint symptoms indicative of OA but without a diagnosis, in a population-based sample. METHODS: Data are from the Canadian Longitudinal Study on Aging (participants aged >45). Individuals reported on doctor-diagnosed OA (hand, hip, or knee) and joint symptoms typical of OA, irrespective of OA diagnosis. A three-level 'OA-status' variable was derived: diagnosed OA; joint symptoms-no OA; no symptoms-no OA (controls). Participants responded to sleep-related questions and were categorized as experiencing insomnia symptoms (yes/no). Logistic regression analysis examined the association between insomnia symptoms and OA status, adjusting for a number of covariates. RESULTS: Of 21,422 respondents, OA was reported by 29.1% (mean age 68.2) and 17.2% reported joint symptoms-no OA (mean age 63.9). One third of those with diagnosed OA and with joint symptoms-no OA reported insomnia symptoms compared to a quarter of controls. Those with OA and with joint symptoms-no OA were similarly more likely to report insomnia symptoms than controls (odds ratio (OR) 1.25, 95% CI 1.17-1.35 and OR 1.32, 95% CI 1.21-1.43). Also significantly associated with insomnia symptoms were female sex, current smoker, lower activity level, multiple chronic conditions and depressive symptoms. Odds ratio magnitudes were greatest for depressive symptoms (OR 2.50, 95% CI 2.26-2.76), comorbidity count (3 + vs. 0 OR 1.68, 95% CI 1.47-1.91) and female sex (OR 1.46, 95% 1.37-1.55). CONCLUSION: Insomnia symptoms were not uncommon among comparatively younger individuals with typical OA joint symptoms and those with OA. This suggests that healthcare providers should address sleep-related issues in those consulting for joint pain, irrespective of diagnosis. Given their high prevalence, this also has implications for sleep-related issues at a population level.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".