Cardiac Bypass Pump Flow Management via NIRS Monitoring
Bibliographic record
Abstract
During cardiac surgery, bypass pumps rely on pressure monitors to evaluate flow. We studied whether it would be possible to optimize pump flow by monitoring changes in cerebral cytochrome a,a3 using NIRS to maintain cyt redox status at its pre‐bypass level. Method: 18 healthy 7–45 kg swine were placed on bypass for repeated cycles of cooling and re‐warming from 36 to 15 to 36°C in 3°C steps. Between each cycle, the swine′s bypass pump blood flow rate was adjusted to restore cytochrome redox status to its pre‐bypass value. Results: In all swine trials, the number of pump flow alterations imposed by NIRS monitoring ranged from 0 to 42, the average being 14 per trial. The best trial had 22 pump flow adjustments during which the range of cytochrome redox status change was 0.50±0.06 μmol l–1. The average trial had a range of cytochrome redox status change of 1.50±0.22 μmol l–1. Conclusion: NIRS‐driven alterations in pump flow rate to maintain pre‐bypass cytochrome redox status can be achieved successfully in the animal model.
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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 | 0.001 |
| 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".