The inflammatory response to cardiopulmonary bypass: a therapeutic overview
Bibliographic record
Abstract
The demographic of cardiac surgery patients continues to evolve to include older, sicker candidates, all the while maintaining an expectation of excellent outcomes. These latter results can only be achieved by the parallel advancement and re-examination of the technology of cardiopulmonary bypass (CPB); the key tool used daily by surgical teams worldwide. In this review, we will provide an overview of integrated therapeutic strategies that can be utilized to minimize the complex and myriad changes related to inflammation after CPB with the understanding that this may abrogate the detrimental end-organ and systemic effects of blood activation. Therapeutic strategies specifically related to the technology can be classified into those targeting biomaterial dependent or independent processes. The former can be addressed by the utilization of currently available biocompatible surfaces such as with heparin-coated circuits, phosphorylcholine-coated circuits ('biomembrane mimicry') and circuits composed of copolymers containing surface-modifying additives. The most important strategies related to biomaterial independent activation include the modification of techniques related to cardiotomy blood management and blood filtration. Finally, all of these strategies must be integrated and tailored with complementary pharmacologic agents such as aprotinin and steroids to optimize anti-inflammatory synergism. Only if we are armed with a comprehensive knowledge of the molecular and cellular basis for these strategies will we be able to continue to evolve our treatment in parallel with our patients to achieve these goals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".