Angina severity predicts worse sleep quality after coronary artery bypass grafting
Bibliographic record
Abstract
OBJECTIVE: We sought to reveal whether the severity of angina pectoris affects sleep quality after elective coronary artery bypass grafting. MATERIAL AND METHODS: Patients scheduled to undergo isolated coronary artery bypass grafting were divided into two groups, having a recent myocardial infarction (Group 1, n=22, mean age 59.40±7.79 years) or not having a recent myocardial infarction (Group 2, n=30, mean age 59.73±7.72 years). The assessment included the Canadian Cardiovascular Society Angina Score, the visual analogue scale for postoperative pain and the Pittsburgh Sleep Quality Index (PSQI). RESULTS: The two groups were similar in regard to baseline characteristics. Cross-clamp time was significantly higher (p=0.007) and the use of inotropes was significantly more common (p=0.01) in those patients with recent myocardial infarction compared to those without. Mean Canadian Cardiovascular Society scores were also higher in patients with recent myocardial infarction (p=0.02). Total Pittsburgh Sleep Quality Index score was significantly higher in patients with recent myocardial infarction (8.45±3.50 vs. 5.03±2.32, respectively, p<0.001). In multivariate analysis, higher angina score (OR: 3.27, 95% CI, 1.20-8.90, p=0.02) and longer time of intensive care unit stay (OR: 6.15, 95% CI, 1.49-25.35, p=0.01) were found to be independent predictors of poor sleep quality. The Canadian Cardiovascular Society angina score showed a significant positive correlation with poor sleep duration score (<0.001), sleep disturbance score (p=0.02), day dysfunction due to sleepiness score (p=0.001), sleep efficiency score (p=0.003), overall sleep quality score (0.03) and total PSQI score (p=0.004). CONCLUSION: The severity of angina pectoris in the preoperative period is independently associated with worse sleep quality after elective isolated coronary artery bypass surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".