Frequent change of procedure during coronary artery bypass surgery suggests insufficient preoperative diagnostic strategy
Bibliographic record
Abstract
We sought to evaluate how often and in what way surgeons change peroperatively their preoperative coronary artery bypass grafting strategy and to what degree these changes affect postoperative graft patency. A series of 109 patients with stable angina pectoris and at least one occluded coronary artery participated. The surgeon filled in a questionnaire pertaining to the planned localization and number of grafts. These estimates were compared to procedures actually performed and with the angiographic outcome six months after bypass surgery. Planned and actually inserted grafts disclosed a discrepancy in 22% of the patients, resulting in a lower or higher number of grafts than pre-operatively estimated. The difference in shift rates between the three sites, left anterior descending, left circumflex, and right coronary artery, was significant (P=0.014). Patency rates were highest when only preoperatively planned grafts were inserted. When shifts occurred, no matter in which direction, it resulted in a decreased patency rate of the inserted grafts. This finding was significant for LAD (P=0.037). Our findings might indicate the necessity of future studies with the use of scintigraphy or fractional flow reserve as physiological adjuncts to angiography for more targeted revascularization.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".