Transfusion thresholds in acute coronary syndromes
Bibliographic record
Abstract
PURPOSE OF REVIEW: Anemia is prevalent in patients with acute coronary syndromes. In this setting, there is uncertainty and controversy surrounding the optimal transfusion strategy for managing anemia. The goal of this review is to summarize the current clinical evidence, guidelines, and future directions for managing transfusion in acute coronary syndromes. RECENT FINDINGS: There is limited evidence from randomized trials evaluating restrictive versus liberal transfusion in patients hospitalized with and/or for acute coronary syndromes. The results from these studies suggest clinical equipoise between transfusion strategies for short term outcomes, and a trend toward favoring a liberal strategy for long term major adverse cardiac events. There is inconsistency across clinical practice guidelines with respect to the optimal strategy for managing anemia and transfusion in acute coronary syndromes due to insufficient evidence. SUMMARY: More evidence is urgently needed to conclusively establish the optimal strategy for transfusion management in the setting of acute coronary syndromes. These data will directly inform harmonization of clinical practice guidelines. Future investigations should explore alternative strategies to hemoglobin for quantifying the degree of anemic stress for personalizing transfusion therapy, the effects on functional outcomes, and managing anemia following hospital discharge.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".