Improving outcomes after low-risk coronary artery bypass grafting: understanding phase of care mortality analysis, failure to rescue and recent perioperative recommendations
Bibliographic record
Abstract
PURPOSE OF REVIEW: Avoidable adverse events are responsible for up to 50% of deaths after low-risk coronary artery bypass grafting. This article reviews recent quality improvement efforts to improve outcomes after cardiac surgery. RECENT FINDINGS: Systematic quality improvement methodology in cardiac surgery has improved significantly over the past decade. Contemporary efforts with phase of care mortality analysis (POCMA) focus on identifying and addressing root causes for mortality. Each patient's perioperative course is an interconnected sequence of clinical events, decisions, interventions, and treatment responses occurring across five perioperative phases. A single seminal event within a specific phase of care has been found to often trigger the eventual death of a patient. Several groups have made significant improvements to perioperative outcomes by addressing these avoidable mortality trigger events. Failing that, failure to rescue (FTR) metrics can be used to identify institutional factors responsible for poor perioperative outcomes. This ongoing focus on quality improvement serves to further improve outcomes after low-risk cardiac surgery. SUMMARY: Modern quality improvement methodology, including POCMA and FTR analysis, has the potential to significantly improve outcomes after cardiac surgery. Larger future studies with multiinstitutional data sharing will be key to facilitate ongoing quality improvement and knowledge translation in this field.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".