265 * TRANSIT-TIME FLOW MEASUREMENT UNMASKS COMPETITIVE FLOW IN ARTERIAL BYPASS GRAFTS
Bibliographic record
Abstract
Objectives: Arterial grafts are preferred; however, competitive flow can diminish efficacy. This study hoped to determine whether transit-time flow measurement (TTFM) could identify competitive flow in arterial grafts. Methods: For 381 consecutive off-pump CABG patients, testing for competitive flow of all grafts possible was performed using an already-in-place proximal snare to occlude native coronary flow after anastomosis construction. TTFM parameters [Flow, pulsatility index (PI), and diastolic filling] were measured with/without proximal snare of the native artery, then analysed retrospectively. Results: From May 2008 to January 2014, 381 consecutive off-pump CABG patients had snare-testing; because of ease/reliability of measurement, 200 single left internal mammary artery-left anterior descending (LIMA-LAD) grafts were analysed. Demographics included: mean age 67 years, male 78%, diabetics 30%, EuroSCORE ≥6–32%. Bilateral internal mammary artery grafting was performed in 71%; 98% grafts were arterial. Upon analysis before/after snaring, 5 patterns emerged (Fig. 1): 1) Flow ↑ and PI ↓↑ or → suggestive of competitive flow seen in moderate stenosis (average 76%) in 27% (54/200) grafts. 2) Flow → and PI → (similar) suggesting no competitive flow, seen in high-grade stenosis (average 79%) in 33% (66/200) grafts. 3) Flow ↓ or → and PI ↑ indicative of possibly suboptimal grafts seen in 11% (22/200) grafts. 4) Flow ↓ and PI → correlated with occluded/high grade stenosis, large retrograde coronary bed (average 88% stenosis) in 18% (36/200) patients. 5) Flow/PI not fitting any pattern (aetiology indeterminate) seen in 23/200 (11.5%). Conclusion: TTFM with the snare-test identifies competitive flow in arterial grafts; patterns of flow/PI values reflect degrees of stenosis in coronary arteries. Used consistently, TTFM is a valuable learning tool for arterial grafting and may help direct conduit choice according to severity of disease.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".