Complex off-pump coronary artery bypass surgery can be safely taught to cardiothoracic trainees
Bibliographic record
Abstract
OBJECTIVES: Off-pump coronary revascularisation is demanding technically as the surgeon is faced with a beating heart and not a bloodless field. The potential clinical advantages of off-pump coronary revascularisation have made this procedure an essential part of a cardiothoracic training program. The aim of this study is to investigate the impact of teaching trainees complex off-pump coronary artery surgery (arterial grafting, 'Y' grafts, sequential grafting and minimally invasive direct coronary artery bypass) on clinical outcomes. METHODS: All 323 off-pump coronary revascularisation cases performed by one service over a 24-month period were analysed. The 125 (39%) operations performed by two trainees with previous exposure in on-pump surgery were compared with the 198 (61%) performed by an experienced consultant surgeon. Patient and disease characteristics, intra- and post-operative data, morbidity and mortality were analysed using uni- and multivariate analysis. RESULTS: The trainees performed 51% of the MIDCABs, 36% of the 'Y' grafts and 27.5% of the sequential grafts. The internal thoracic artery was used in 96% of the cases, radial artery in 49% and bilateral internal thoracic arteries in 11% equally distributed between trainees and consultant. The average number of grafts per case was 3.7 for the consultant and 3.3 for the trainees. Patients operated by the consultants were more likely to have unstable angina (P=0.008), ejection fraction <30% (P=0.01) previous cardiac surgery (P=0.027) and more likely to receive over 4 grafts (P=0.01). Operative mortality was 1.5% for the consultant and 0 for the trainees (P=0.17). Post-operative morbidity, such as re-operation for bleeding (consultant 1% vs. trainee 0.8%), stroke (0.5% vs. 0.8%), haemofiltration (3.5% vs. 0.8%) was similar between the two groups. Hospital stay was also similar. CONCLUSIONS: The results of this study suggest that trainees under supervision perform complex off-pump coronary artery surgery safely with low rate of mortality and complications. These findings are in agreement with previous literature reports. Trainees should be allowed to operate on sufficient number of patients undergoing off-pump surgery according to their skills and abilities. Patients should be reassured that safety is not compromised by the presence of a trainee as a primary surgeon.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".