Transit-time flow predicts outcomes in coronary artery bypass graft patients: a series of 1000 consecutive arterial grafts☆
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to evaluate transit-time flow (TTF) as a tool to detect technical errors in arterial bypass grafts intra-operatively and predict outcomes. METHODS: TTF's three parameters, pulsatility index (PI, index of resistance), flow (cc min(-1)) and diastolic filling (DF, proportion of diastole with coronary flow), were measured in 990/1000 (99%) of arterial grafts in 336 consecutive patients, prospectively enrolled in a database. Grafts were revised when TTF findings supported the otherwise suspected graft malfunction. If no other signs/suspicion of graft malfunction existed (normal electrocardiogram (EKG), stable haemodynamics and unchanged ventricular function on trans-oesophageal echocardiography (TEE)), and the PI was >5, grafts were not revised. Major adverse cardiac events (MACEs: recurrent angina, perioperative myocardial infarction, postoperative angioplasty, re-operation and/or perioperative death) were related to TTF measurements. RESULTS: The average number of grafts per patient was 3.02, of which 99% were arterial. Satisfactory grafts were achieved in 916/990 (93%) of the grafts, with flows from 34 to 61 cc min(-1), PI < or =5 and DF of 62-85%. Fourteen conduits, 20 grafts (2%) suspected to be problematic, were revised. Patients were divided into two groups: 277 (82%) with at least one graft with PI < or =5 and 59 (18%) with a PI >5. MACE occurred in 25 (7.4%) patients--15/277 patients with a PI < or =5 (5.4%) and 10/59 with a PI >5 (17%, p=0.005). Mortality following non-emergent surgery was significantly higher in patients with a PI >5 (5/54, 9%) than in patients with a PI < or =5 (5/250, 2%, p=0.02). Flow and DF were not predictive of outcomes. CONCLUSION: A high PI predicts technically inadequate arterial grafts during surgery--even if all other intra-operative assessments indicate good grafts; it also predicts outcomes, particularly mortality.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".