Bibliographic record
Abstract
Purpose To assess the predictive risk factors for early morbidity and mortality after coronary artery bypass grafting (CABG). Methods Data from 108 consecutive patients who underwent coronary artery bypass grafting in a single center were retrospectively collected.The pre and intra operative predictive variables were assessed with chi square and Logistic stepwise regression analysis to predict early postoperative morbidity and mortality of the patients. Results The predictive risk factors those significantly associated with postoperative heart failure were New York Heart Association class status (NYHA) Ⅲ and Ⅳ,cardiopulmonary hypass time (CPBT) and aortic cross clamping time (ACCT),those significantly associated with postoperative respiratory insufficiency were CPBT and ACCT,and those significantly associated with postoperative mortality were NYHA status Ⅲand Ⅳ,recent myocardial infarction (RMI),CPBT,ACCT and body surface area1.80.The predictive risk factors those significantly associated with both postoperative morbidity and mortality were angina CCS classⅢand Ⅳ,RMI,CPBT,cold heart surgery (CHS).Analyzing with Logistic regression,the preoperative angina CCS class (odds ratio,4.16,95% confident interval 1.07-16.17, P =0.04),CHS(OR,3.68,CI?1.22-11.14, P =0.02)and CPBT (OR,1.28,CI?1.02-1.60, P =0.03) were demonstrated to be the predictive risk factors that significantly associated with postoperative morbidity and mortality. Conclusions The preoperative heart function and technique of coronary artery surgery were the most important prdictive risk factors that associated significantly with early postoperative morbidity and mortality.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".