Unstable angina does not increase mortality in coronary artery bypass graft surgery
Bibliographic record
Abstract
INTRODUCTION: Coronary artery bypass graft is often the treatment of choice for patients who suffer from unstable angina. We do not know whether this condition adds morbidity in this scenario. OBJECTIVE: To compare the outcomes of patients undergoing coronary artery bypass graft with unstable angina framework with patients who underwent coronary artery bypass graft showed no unstable angina. METHODS: Retrospective cohort study. Unstable angina was defined as acute coronary syndrome without ST elevation and without enzymatic alteration and/or class IV angina. RESULTS: Between February 1996 and July 2010, to 2,818 isolated coronary artery bypass graft performed, 1,016 (36.1%) patients had unstable angina. Multivariate analysis showed that patients with preoperative unstable angina used more medications such as acetylsalicylic acid, beta-blocker, heparin (anticoagulation), nitrate and less need for diuretics than patients without unstable angina. Patients with unstable angina used increased monitoring with Swan-Ganz and support with intra-aortic balloon than stable patients. On outcomes, required longer hospitalization (P=0.030) and had a lower death rate (P=0.018) in the post-coronary artery bypass graft alone. CONCLUSION: Submit patients to coronary artery bypass graft in the presence of acute coronary syndrome such as unstable angina did not increase the mortality rate.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".