Laxatives are Associated with Poorer Polysomnography-derived Sleep Quality
Bibliographic record
Abstract
ABSTRACT: Objective: To characterize 1) the relationship between laxative use and objective sleep metrics, and 2) the relationship between laxative use and self-reported insomnia symptoms in a convenience sample of middle-aged/elderly patients who completed in-laboratory polysomnography. Methods: We cross-sectionally analyzed first-night diagnostic in-laboratory polysomnography data for 2946 patients over the age of 40 (mean age 60.5 years; 48.3% male). Laxative use and medical comorbidities were obtained through self-reported questionnaires. Patient insomnia symptoms were based on self-report. Associations between laxative use and objective sleep continuity were analyzed using multivariable linear regression models. Associations between laxative use and insomnia were assessed using multivariable logistic regression models. Results: After adjusting for age, sex, body mass index, total recording time, and relevant comorbidities, laxative users had a 7.1% lower sleep efficiency (p < 0.001), 25.5-minute higher wake after sleep onset (p < 0.001), and a 29.4-minute lower total sleep time (p < 0.001) than patients not using laxatives. Laxative users were found to be at greater odds of reporting insomnia symptoms (OR = 1.7, p = 0.024) than patients not using laxatives. Conclusion: Laxative use is associated with impairments in objective sleep continuity. Patients using laxatives were also at greater odds of reporting insomnia symptoms.
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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 | 0.000 | 0.003 |
| 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".