Volume and outcome of coronary artery bypass graft surgery: are more and less the same?
Bibliographic record
Abstract
BACKGROUND: Despite the relatively high degree of regionalization of coronary artery bypass graft surgery in Canada, support is expressed for even further centralization of this procedure. The finding that the risk-adjusted mortality rate is lower at high volume hospitals is the basis for the decision to centralize. The goal of this study was to determine if current evidence supports the extension of such a policy. METHODS: A systematic review of the literature (1980 to 2002) provided mortality and surgical volume studies on 21 patient cohorts. For 16 of these, the published information permitted division of each cohort into those who had surgery at high or low volume hospitals. The target level for division was 200 cases per year; the level achieved was 200+/-44 (mean+/-SD). The odds ratio (OR) was calculated from the observed and expected mortality ratios. In seven studies, volume had been treated as a continuous variable; the effect of volume was expressed as OR per 100 patients in four of these studies. RESULTS: A plot of OR against year of surgery showed a progressive increase from 0.55 (favouring high volume) in 1972 to 0.95+/-0.07 for the past few years. All estimates of OR per 100 patients were very close to 1.0, also indicating little or no effect of volume on mortality. INTERPRETATION: These results are compatible with the concept that with time the lower mortality associated with high volume has been virtually eliminated. It is hypothesized that this development is explained by a multifaceted learning curve, improved surgical training and technical advances. Therefore, the current evidence does not provide a basis for further regionalization of cardiac bypass 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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".