Pharmacological strategies for blood conservation in cardiac surgery: erythropoietin and antifibrinolytics.
Bibliographic record
Abstract
PURPOSE: We review the clinically important benefits of the two principal pharmacological strategies, erythropoietin (EPO) and antifibrinolytics (aprotinin and lysine analogues), to decrease transfusion of allogeneic blood products (ABP) during and after cardiac surgery. SOURCE: Articles were selected from an ongoing review of the literature, with special attention to meta-analyses dealing with EPO and/or antifibrinolytics and cardiac surgery. PRINCIPAL FINDINGS: The few studies available include a number of patients insufficient to allow definitive conclusions on the benefits of EPO in cardiac surgery. Further studies are required to determine the optimal dose of EPO and to compare its cost-effectiveness with other blood sparing strategies in this context. Both aprotinin and lysine analogues effectively decrease ABP transfusions and the incidence of re-thoracotomy. In addition, high-dose aprotinin reduces cerebrovascular morbidity and mortality after cardiopulmonary bypass. Several mechanisms have been put forward to explain these beneficial effects, some of which could well be common to all antifibrinolytics. The clinical benefits of aprotinin's unique anti-inflammatory effect are not entirely clear but the finding that it reduces the incidence of stroke and death is certainly a major argument in favor of its utilization. Yet, we have to ensure that aprotinin's benefits are not offset by side-effects such as allergy. CONCLUSIONS: We still need large scale studies to definitely confirm the benefits and exclude the deleterious effects of these drugs on outcomes other than ABP requirements. At present, aprotinin is the only agent that has been shown to reduce the risk of cerebrovascular accident and mortality after cardiac surgery in adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".