Abstract 11334: Long-Term Survival and Quality of Life of Patients Submitted to Emergency Coronary Artery Bypass Grafting for Post-Infarction Cardiogenic Shock
Bibliographic record
Abstract
Background: This study was intended to evaluate the long-term outcome of patients submitted to emergency coronary artery bypass (eCABG) for postinfarction cardiogenic shock (post-AMI CS). Methods: Sixty-seven consecutive patients were submitted to eCABG for post-AMI CS at two European institutions during an 11 year period. Pre-, intra-, postoperative and long-term follow-up data of all patients were prospectively collected. Results: Hospital survival was 86% (58/67) with all deaths due to cardiac causes. At a mean follow-up of 78±48 months (range 1-153) 43 of the 58 patients discharged from the hospital were alive (74%); 9 of the 15 late deaths (60%) were non cardiac. Overall survival rate at the end of follow-up was 64% (43/67). The majority (41/43, 95%) of survivors were in NYHA class I-II, ischemia free, had a Karnofsky performance status > 80 and an excellent quality of life basing on the Seattle Angina Questionnaire. The use of the internal thoracic artery (Figure 1) and of cardiopulmonary bypass (Figure 2) were both associated with significantly better long-term survival (p=.01 for both). Conclusions: The long-term survival and quality of life of patients submitted to eCABG for post-AMI CS are good; eCABG should be considered a valuable therapeutic option in this setting. The use of cardiopulmonary bypass and of the internal thoracic artery at the time of surgery are strongly advocated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".