The effect of using microplegia on perioperative morbidity and mortality in elderly patients undergoing cardiac surgery
Bibliographic record
Abstract
Old age is a significant risk factor for perioperative morbidity and mortality following cardiac surgery and optimal myocardial protection strategy should be sought in this group of patients. We, therefore, reviewed the data on 295 consecutive patients older than 75 years who underwent any cardiac surgical procedure. Microplegia was used in 144 patients compared to 151 patients who had the standard 4:1 blood cardioplegia. Logistic regression analysis was used for propensity matching to balance the differences between the two groups. The microplegia group included more females and sicker patients as indicated by higher Parsonnet scores. There were differences in the pump time, aortic cross-clamp time, procedure type and surgeons between the two groups. These differences were balanced using the propensity matching. In-hospital mortality, acute renal injury and confusion were higher in the microplegia group (17%, 34%, 35%, respectively) compared to the standard 4:1 cardioplegia group (9%, 23%, 24%, respectively) (P=0.04, 0.04, 0.04, respectively). These differences were not statistically significant after propensity matching. These results demonstrate that the use of microplegia is safe in patients older than 75 years who are undergoing cardiac surgery and results in similar in-hospital morbidity and mortality to the standard 4:1 blood cardioplegia.
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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 | 0.005 |
| 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".