Factors affecting mortality after coronary bypass surgery: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Previous research reports numerous factors of post-operative mortality in patients undergoing isolated coronary artery bypass graft surgery. However, this evidence has not been mapped to the conceptual framework of care improvement. Without such mapping, interventions designed to improve care quality remain unfounded. METHODS: We identified reported factors of in-hospital mortality post isolated coronary artery bypass graft surgery in adults over the age of 19, published in English between January 1, 2000 and December 31, 2019, indexed in PubMed, CINAHL, and EMBASE. We grouped factors and their underlying mechanism for association with in-hospital mortality according to the augmented Donabedian framework for quality of care. RESULTS: We selected 52 factors reported in 83 articles and mapped them by case-mix, structure, process, and intermediary outcomes. The most reported factors were related to case-mix (characteristics of patients, their disease, and their preoperative health status) (37 articles, 27 factors). Factors related to care processes (27 articles, 12 factors) and structures (11 articles, 6 factors) were reported less frequently; most proposed mechanisms for their mortality effects. CONCLUSIONS: Few papers reported on factors of in-hospital mortality related to structures and processes of care, where intervention for care quality improvement is possible. Therefore, there is limited evidence to support quality improvement efforts that will reduce variation in mortality after coronary artery bypass graft surgery.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".