Effect of perfusionist technique on cerebral embolization during cardiopulmonary bypass
Bibliographic record
Abstract
OBJECTIVE: To determine the association between high-intensity transient signals (HITS) and perfusionist interventions, purging techniques, pump flows and venous reservoir blood volume levels during cardiopulmonary bypass. METHODS: Transcranial Doppler was used to detect HITS in the middle cerebral artery during the period of aortic crossclamping in patients undergoing coronary artery bypass grafting. Perfusionist-related interventions were recorded and included blood sampling (including the number of times that the oxygenator sampling manifold was purged), drug bolus injections and infusions (vasopressors, crystalloid and mannitol). Pump flows and venous reservoir volume levels were also documented. RESULTS: There were 534 interventions in 90 patients [median number of interventions per patient: 6 (quartiles: 4, 8)]. The median total HITS count from all interventions was 17 (5, 37). This represented 38% of the total HITS counts during aortic crossclamping. Factors contributing to differences in the HITS count included type of intervention (p <0.0001) and perfusionist (p =0.0012). Blood sampling (p<0.001) and drug bolus injections (p=0.06) had higher HITS counts per patient than infusions. Repetitive purging significantly increased HITS counts (r=0.74; p <0.001). Purging perfusionists (purging: 1-10 times) had higher HITS counts per patient [5 HITS (1, 15) than nonpurgers [0 HITS (0, 1) p <0.0001]. HITS counts were significantly correlated with reservoir volumes (r= -0.20, p=0.017) and pump flow rates (r=0.21, p =0.008). Reservoir volume levels < or =800 mL were associated with higher HITS counts per intervention [11 HITS (2, 27)] during blood sampling compared with higher volume levels [3 HITS (1, 10), p =0.001]. CONCLUSIONS: Cerebral emboli associated with perfusionist interventions can be minimized by not purging the sampling manifold, using continuous infusions rather than bolus injections, and maintaining high blood-volume levels (>800mL) in the venous reservoir.
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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 | 0.001 | 0.010 |
| 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".