Bibliographic record
Abstract
We agree that cardiopulmonary bypass activates the many plasma protein systems including those listed and certain blood cell types including platelets, neutrophils, monocytes, endothelial cells, and lymphocytes. We also agree that these changes, along with the numerous neurohumoral vasoactive substances that are released during CPB can have a marked effect on vasomotor tone and vascular permeability after cardiac surgery. These vascular changes can markedly affect the recover of patients after cardiac surgery. Our review of vasomotor dysfunction after cardiac surgery was not intended to be an exhaustive review of the etiology of all pathologic changes that occur during cardiac surgery, but rather briefly review the changes that occur in the regulation of vascular tone only. The pathologic processes leading to altered vascular tone and permeabilty are still relatively poorly understood. While some of the processes listed by Dr Sameh do undoubedly account for some of the vascular alterations observed, there is little definitive proof of this for all cases. It was hoped by many that off bypass (OPCAB) coronary revascularization might lessen the vasomotor changes that occur after cardiac surgery, but this in many cases this has not been observed. Because of a lack of definitive information regarding the cause of vascular changes after cardiac surgery, further investigation will be necessary to fully elucidate the answer. We appreciate your comments.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.037 | 0.047 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".