Transit time flow measurement of coronary bypass grafts before and after protamine administration
Bibliographic record
Abstract
BACKGROUND: Intraoperative graft assessment with tools like Transit Time Flow Measurement (TTFM) is imperative for quality control in coronary surgery. We investigated the variation of TTFM parameters before and after protamine administration to identify new benchmark parameters for graft quality assessment. METHODS: The database of the REQUEST ("REgistry for QUality AssESsmenT with Ultrasound Imaging and TTFM in Cardiac Bypass Surgery") study was retrospectively reviewed. A per graft analysis was performed. Only single grafts (i.e., no sequential nor composite grafts) where both pre- and post-protamine TTFM values were recorded with an acoustical coupling index > 30% were included. Grafts with incomplete data and mixed grafts (arterio-venous) were excluded. A second analysis was performed including single grafts only in the same MAP range pre- and post- protamine administration. RESULTS: After adjusting for MAP, we found a small increase in MGF (29 mL/min to 30 mL/min, p = 0.009) and decrease in PI (2.3 to 2.2, p < 0.001) were observed after the administration of protamine. These changes were especially notable for venous conduits and for CABG procedures performed on-pump. CONCLUSION: The small changes in TTFM parameters observed before and after protamine administration seem to be clinically irrelevant, despite being statistically significant in aggregate. Our data do not support a need to perform TTFM measurements both before and after protamine administration. A single TTFM measurement taken either before or after protamine may suffice to achieve reliable data on each graft's performance. Depending on the specific clinical situation and intraoperative changes, more measurements may be informative. TRIAL REGISTRATION: Clinical Trials Number: NCT02385344 , registered February 17th, 2015.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".