Transit-time flow predicts outcomes in coronary artery bypass graft patients: a series of 1000 consecutive arterial grafts☆
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it