Conversion after off-pump coronary artery bypass grafting: the CORONARY trial experience
Bibliographic record
Abstract
Objectives: Emergent and late conversions form OFF-to-ON pump coronary artery bypass grafting (CABG) have been associated with worse outcomes, however, it remains unclear as to which risk factors are associated with conversion and how to prevent them. Methods: Among 4718 patients who randomly underwent off- or on-pump CABG, the incidence of off-pump to on-pump cross-over, or 'OFF-to-ON conversion', was 7.9% (186/2356). The primary outcome was a composite of death, stroke, myocardial infarction, or new renal failure requiring dialysis. We assessed the risk factors and outcomes of converted patients. Results: Emergent OFF-to-ON conversions, defined as conversions for hypotension or ischaemia, were required for 3.2% of patients ( n = 75), while most elective conversions were due to small or intramuscular coronaries ( n = 83). OFF-to-ON converted patients required increased surgery time, blood transfusions, intensive care unit stay, and presented a higher incidence at 1 year of the composite outcome compared with non-converted off-pump patients (all P < 0.01), especially if the conversion was emergent. Conversely, elective conversions outcomes were no different compared with non-converted off-pump patients ( P = 0.35). Independent predictors of emergent conversions included higher heart rate or chronic atrial fibrillation, urgent surgery, more grafts planned and surgeon experience with off-pump CABG. Conclusions: Emergent OFF-to-ON conversion is associated with worse outcomes compared with elective conversion or no conversion. In the presence of risk factors for emergent conversion, an early and elective conversion approach is a judicious strategy.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".