Cardiopulmonary bypass technology transfer: musings of a cardiac surgeon
Bibliographic record
Abstract
The development of cardiopulmonary bypass (CPB) has been one of the greatest technical advancements in cardiovascular medicine. With heparin anticoagulation, this device can safely replace the circulatory and gas-exchanging functions of the heart and lung, facilitating complex cardiac operations. Limitations still exist however, related to blood reactions at the biomaterial surface, such as cell activation, inflammation and low-grade thrombosis. In this brief review, the thought processes which paralleled the development of CPB biocompatible surfaces such as heparin-coating, will be explored, as well as current theories on the suspected mechanisms by which heparin-coated surfaces act as an anti-inflammatory device during CPB. Results with new surfaces for CPB designed to capitalize on superior protein adsorption properties, such as surface modifying additive (SMA) and poly (2-methoxyethylacrylate) (PMEA), will also be described. Finally, the significance of biomaterial-independent blood activation will be discussed, emphasizing the current need to develop strategies utilizing optimal biomaterials, modified surgical technique and pharmacologic therapy to minimize the systemic complications of CPB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".