Intraoperative graft flow measurements during coronary artery bypass surgery predict in-hospital outcomes
Bibliographic record
Abstract
Transit-time flowmetry enables immediate intraoperative assessment of blood flow parameters in coronary artery bypass grafts (CABG). The present study assesses the predictive value of measured graft flows on early and medium-term outcomes. All cardiac surgery patients with measured graft flows were included. The last intraoperative flow measurements recorded using the Medtronic Butterfly Flowmetry system were used for analysis. Patients were separated into two groups: patients with normal flow in all grafts or patients with abnormal flow > or =1 graft. Any pulsatility index (pulsatility index=min-max flow/mean flow) < or =5 was determined to be normal flow. The study population included 985 patients. Nineteen percent of patients had abnormal flow in > or =1 graft. Overall in-hospital mortality was 4.7% and not significant between the two groups. After adjusting for covariates, the in-hospital composite outcome for adverse cardiac events was more prevalent in the abnormal flow group (31% vs. 17%; P<0.0001) with an odds ratio of 1.7 (CI 1.1-2.7). Survivors to discharge had a mean follow-up of 1.8 years. However, abnormal flow was not an independent predictor of the medium-term mortality and readmission to hospital for cardiac reason following discharge. Our findings suggest that abnormal flows measured intraoperatively are independently associated with short-term in-hospital adverse outcome.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".