Is There a Relationship between Pressure Gradients through Contemporary Oxygenators and Immune Cell Proliferation during Cardiopulmonary Bypass? A Pilot Study
Bibliographic record
Abstract
There have been many advances in the perfusion equipment used for cardiopulmonary bypass (CPB) surgery. A key component, the membrane oxygenator, has had a number of modifications in recent years and a recent clinical evaluation demonstrated disparity in various aspects of device performance. One difference among oxygenators, which to-date has received little attention, was the impact on the patient’s immune cells, with some oxygenators producing a significantly greater increase in immune cell numbers after cross clamp. Such increases in immune cell proliferationmaycontribute to the development of a systemic inflammatory response (SIR), which has been demonstrated to have a negative impact on patient outcomes. Although factors contributing to immune cell proliferation during CPB are recognized to be multi-factorial, the goal of the current communication was to perform an ad hoc analysis of these raw data for evidence that pressure gradients through an oxygenator contributes to this outcome. Despite the observation that higher-pressure gradient oxygenators appeared to associate with increased immune cell proliferation, no correlation was detected in this analysis. This finding, however, provides further evidence for the complex nature of inflammation during CPB, which deserves ongoing discussion and investigation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".