Safety and Efficacy of Methods for Reducing Perioperative Allogeneic Transfusion: A Critical Review of the Literature
Bibliographic record
Abstract
A number of pharmacologic and nonpharmacologic technologies are in current use to minimize perioperative homologous blood use. Clinical trials, many of them randomized controlled trials, have been done evaluating these approaches and have demonstrated their efficacy. However, data on safety has relied mostly on case reports, uncontrolled studies, and, for the pharmacologic agents, extrapolation from the nonsurgical setting. In this review I analyze the data from the randomized trials and the lower-level evidence studies to provide the best estimates in safety with these alternatives. In general, these alternatives are safe with proper dosing and monitoring of effects. With aprotinin, the primary concern is anaphylaxis, and this predominantly with re-exposure. With aprotinin and with the anti-fibrinolytics, increased venous thromboembolic risk has not been a consistent finding. Tranexamic acid use intraoperatively is advantageous, but postoperative use appears to have no advantage and may be associated with renal dysfunction. DDAVP is low-risk, provided it is not overused, which can induce hyponatremia. Autologous predonation probably has similar risks as homologous blood with respect to transfusion errors and bacterial infection. As with most medical interventions, we must be vigilant to prevent human error.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".