Does performing cardiac surgery after hours impact postoperative outcomes? A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: There has been concern regarding the safety of cardiac surgical intervention during off-hours. Sleep deprivation, resource limitations, and an increased case urgency have been postulated to increase off-hours surgical risk, although outcomes are inconsistent in the existing literature. In this systematic review and meta-analysis, we review the literature comparing patients undergoing cardiac surgery during on and off-hours. EVIDENCE ACQUISITION: PubMed and Embase were systematically searched for literature published from January 2000-September 2023, comparing outcomes of patients undergoing cardiac surgery during on and off-hours. Overall, 3540 manuscript titles and abstracts were screened and 11 articles were included. EVIDENCE SYNTHESIS: Overall aggregate analysis indicated no significant differences in rates of in-hospital mortality(OR 1.04; 95% CI, 0.41-2.63; P=0.93) and perioperative morbidity, including stroke (P=0.52), reoperation (P=0.92), major bleeding (P=0.10), and renal complications (P=0.55). Composite rates of sternal wound infection favored on-hours surgery (P=0.01). CONCLUSIONS: Although inferior outcomes in patients undergoing cardiac surgery during off-hours have been noted, aggregate analysis largely revealed equivalent perioperative morbidity and mortality during on and off-hours surgery, although with the exclusion of one outlier study in-hospital mortality and reoperation favored on-hours surgery. Heterogeneity in outcomes is likely multifactorial, with surgical staff fatigue, patient preoperative risk, clinical setting, and resource limitations all contributing. Further investigation is required directly comparing emergent cardiac surgical intervention during on-hours and off-hours controlling for baseline surgical risk to elucidate the true impact of timing of surgery on postoperative outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.050 | 0.124 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".