In hypertensive individuals, sleep time and sleep efficiency did not affect the number of angina episodes: a cross-sectional study
Bibliographic record
Abstract
Abstract Previous studies have reported adverse effects of short and long sleep duration on cardiovascular health. However, how sleep time and sleep efficiency affect angina have not been studied in hypertensive individuals. This study aimed to assess the relationship of sleep with angina. Using a cross-sectional design, data from 1563 hypertensive individuals were collected from the parent Sleep Heart Health Study (SHHS). Age, alcohol use, average diastolic blood pressure (ADBP), average systolic blood pressure (ASBP), cigarette use, sleep time, sleep efficiency, percent time in stage N3 of sleep, and body mass index (BMI) were used as covariates. Multiple linear regression, the Chi-Square test, and Pearson’s correlation coefficient were used for data analysis. Unadjusted sleep efficiency, sleep time, ADBP, and age were significant (p < 0.05) predictors of the number of angina episodes (Anginan). When the covariates were adjusted, only ADBP and ASBP were significant (p < 0.05) predictors of Anginan. Sleep efficiency, BMI, ADBP, sleep time, and age had a significant (p < 0.05) correlation with Anginan. In hypertensive individuals, sleep time and sleep efficiency did not affect Anginan when adjusted for covariates. ADBP and ASBP were found to be significant predictors of Anginan when the covariates were adjusted.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".