Detrimental predictive effect of metabolic syndrome on postoperative complications in patients who undergoing coronary artery bypass grafting.
Bibliographic record
Abstract
BACKGROUND: The present study came to address the value of metabolic syndrome (MetS) in predicting postoperative outcome following coronary artery bypass grafting (CABG). METHODS: In a retrospective study, a consecutive series of patients including 2010 subjects who underwent isolated CABG were reviewed. Baseline information and intraoperative details were collected by reviewing hospital-recorded files. The composite outcome of major adverse cardiac and cerebrovascular events (postoperative morbidity) was generated from the occurrence of myocardial infarction, cardiac arrhythmias, stroke, renal failure, and other cardiac-related problems. RESULTS: Overall, 2010 patients who underwent isolated CABG were studied that among them 24.7% suffered from MetS. No difference was found in the prevalence of postoperative arrhythmias, brain stroke, multi-organ failure, and dialysis between the two groups with and without MetS. Early morbidity rate was 27.4% in MetS group and 27.8% in non-MetS group with no significant discrepancy. Using multivariable logistic regression modeling, we showed that MetS status could not predict postoperative morbidity; however, advanced age, history of congestive heart failure, higher Canadian Cardiovascular Society (CCS) scale, and longer cross-clamp time were main indicators of postoperative morbidity. CONCLUSION: MetS has no detrimental predictive effect on early postoperative morbidity in CABG patients. (www.actabiomedica.it).
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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 | 0.001 |
| 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) | 0.001 | 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".