Counterpoint: the design and interpretation of blood transfusion randomized clinical trials
Bibliographic record
Abstract
ABSTRACT: A recent Perspective in Blood suggested that previous evidence from over a decade ago established that a liberal rather than a restrictive blood transfusion strategy results in better outcomes in patients with anemia and either acute myocardial infarction or stable cardiovascular disease. Their premise was that physiological evidence, and a different interpretation of the Transfusion Requirements in Critical Care (TRICC) trial should have been sufficient to establish clinical practice. They also suggest that a more personalized approach to the administration of transfusions would have been made possible by including a usual-care arm in all transfusion trials. In this counterpoint Perspective, we describe how and why 2 discrete and common blood transfusion thresholds were selected in the TRICC, FOCUS, REALITY, and MINT trials. We explain why a usual-care arm would have been uninformative. We also propose that we still do not have evidence to provide firm transfusion recommendations in several specific subpopulations of patients, including those with stable atherosclerotic coronary artery disease. Finally, we provide our perspective on the state of existing evidence and on the clinical recommendations that should be adopted in practice.
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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 | 0.314 | 0.527 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".