Does the Trainee's Level of Experience Impact on Patient Safety and Clinical Outcomes in Coronary Artery Bypass Surgery?
Bibliographic record
Abstract
BACKGROUND: There is a relative dearth of information on how the resident's level of training affects patient outcomes in cardiac surgery. We designed this study to determine if there were any significant differences in patient demographics and clinical outcomes of coronary artery bypass procedures (CABG) performed by residents of PGY 4/lower, residents of PGY 5/6, fellows, or consultants. METHODS: Standardized preoperative, intraoperative, and postoperative variables were prospectively collected and analyzed on 2906 isolated CABG procedures, performed between July 1999 and March 2006 with the primary surgeon prospectively classified as PGY4/lower, PGY5/6, fellow, and consultant. RESULTS: The number of cases performed by residents of PGY4/lower, PGY5/6, fellows and consultants were 179, 263, 301, and 2163, respectively. Preoperative demographics and comorbidities were similar except PGY4/lower group had more diabetics and consultant group had more patients requiring IABP. More non-LIMA arterial conduits were used in the consultant and fellow groups. However, there were neither significant differences in the mean number of grafts nor in the composite postoperative morbidity, median ICU, and hospital lengths of stay. Observed in-hospital mortality was 2.2%, 1.5%, 1.7%, and 2.7% (p = 0.49), respectively. CONCLUSIONS: Preoperative patient demographics and operative data were similar in all groups except that patients requiring IABP preoperatively were more likely operated on by consultants and arterial revascularization was performed more commonly by consultants and fellows. Postoperative mortality and morbidity rates were similar among all groups, thus demonstrating that with appropriate supervision, trainees of all levels can safely be taught CABG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".