The use of intraoperative graft assessment in guiding graft revision
Bibliographic record
Abstract
Quality assurance (QA) in medicine is the practice of the prevention of errors and avoiding problems when delivering care in the form of medical therapy, both in terms of non-invasive and invasive procedures. It is rightly expected by patients. Up until the last 10 years, verification of intraoperative bypass graft patency was limited to a stable hemodynamic status, lack of electrocardiographic evidence of myocardial infarction and, if available, no new regional wall motion abnormalities on transesophageal echo. This perspective outlines two technologies for QA during coronary artery bypass graft (CABG) surgery: transit-time flow measurement (TTFM) for functional assessment of coronary grafts and anatomical evaluation with epicardial ultrasound (ECUS). TTFM is a seasoned technology, used since the late 1990s. ECUS is relatively new, used since 2012. TTFM alone, although useful for intraoperative bypass graft assessment, is not enough; 10-15% of graft values are ambiguous as to the efficacy of graft function. Therefore, although newer, ECUS is already being established as an indispensable tool for quality assessment in coronary surgery. The two modalities combined are vital for 'state of the art' intraoperative bypass graft assessment.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".