Relationship between sleep pattern and bone mineral density in patients with osteoporotic fracture
Bibliographic record
Abstract
Background: Evidence investigating sleep pattern in relation to bone health in elderly participants with osteoporosis remains sparse. We aimed to assess the relationship between sleep pattern incorporating five sleep characteristics (snoring, midnight waking up, insomnia, sleep duration, and daytime napping) and bone mineral density (BMD) in elderly participants with osteoporotic fracture. Methods: A cross-sectional study was conducted to include eligible elderly patients from the Department of Orthopedics who were admitted to hospital due to an osteoporotic fracture. Sleep pattern was constructed based on total sleep scores and categorized into healthy, intermediate, and poor pattern groups. Multivariable logistic regression model was used to assess sleep pattern in relation to risk of low BMD. Results: A total of 169 elderly patients with osteoporotic fracture were included in this study (mean age: 71.91 years; 87.57% females). There were 36 (21.30%), 107 (63.31%), and 26 (15.38%) patients with healthy, intermediate, and poor sleep pattern, respectively. Compared with healthy sleep pattern, no significant relationship between intermediate sleep pattern and BMD was detected [odds ratio (OR) = 1.72, 95% confidence interval (CI): 0.74, 3.97, p = 0.21), while poor pattern was significantly associated with decreased BMD (OR = 3.50, 95% CI: 1.10, 11.14, p = 0.034). Conclusion: The majority of elderly patients with osteoporotic fracture had unhealthy sleep pattern; poor sleep pattern was significantly related to reduced BMD when compared with healthy pattern. Further high-quality evidence is needed to assess and validate the relationship between sleep pattern and risk of low BMD in the elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".