Transfusion‐related adverse events in patients with restrictive or liberal transfusion strategy. An analysis of the <scp>MINT</scp> trial
Bibliographic record
Abstract
BACKGROUND: The aim of this analysis was to quantify and compare the frequency of transfusion-related adverse events (TRAE) in adult patients with myocardial infarction transfused with a restrictive versus liberal transfusion strategy from the Myocardial Ischemia and Transfusion (MINT) trial. STUDY DESIGN AND METHODS: Clinical sites reported TRAE. Major TRAE were transfusion-related acute lung injury, transfusion-associated circulatory overload, acute hemolytic transfusion reaction, anaphylactic transfusion reaction, and transfusion-associated sepsis. TRAE rates per transfusion arm per patient and per 100 units of red blood cells (RBC) transfused were calculated. RESULTS: There were nine site-reported events in the restrictive transfusion arm and 49 events in the liberal transfusion arm with an overall event rate of 0.51 in the restrictive transfusion arm, and 2.80 in the liberal transfusion arm per 100 patients, rate difference -2.29; 95% confidence interval [CI], -3.12,-1.44.. The rate of major TRAE was 0.51 in the restrictive arm and 1.99 in the liberal arm per 100 patients, rate difference -1.48 (95% CI, -2.21, -0.74). When the rates were normalized per 100 units of RBC transfused, the rate difference of major TRAE was -0.08 (95% CI, -0.63, 0.46) and rate ratio was 0.90 (95% CI, 0.43, 1.87). CONCLUSION: The rate of major TRAE was low, albeit increased in the liberal transfusion arm proportional to the number of RBC units transfused. The higher rate of major TRAE in the liberal arm was not sufficient to offset the increased rates of myocardial infarction or death at 30 days observed in patients assigned to the restrictive transfusion arm.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".