Transfusion in Anemic Patients With Acute Coronary Syndromes: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: There is controversy surrounding the effectiveness of red blood cell (RBC) transfusion for treating anemia in patients hospitalized for acute coronary syndromes (ACS), particularly as hemoglobin (Hb) levels approach and drop below the range of moderate anemia. METHODS: This population-based cohort study followed all adults hospitalized for ACS who experienced an in-hospital nadir Hb between 6.0 and 8.9 g/dL between April 1, 2012, and March 31, 2021, in Ontario, Canada. Patients were excluded if they underwent coronary artery bypass graft surgery or had history of dementia, palliative care, or long-term care. Transfused patients were compared with nontransfused patients. The primary outcome was a composite of all-cause death and hospitalization for myocardial infarction (MI) within 30-days of hospital discharge. Overlap propensity score weighting was used to account for confounding and to emphasize the comparison in patients for whom there is clinical equipoise. RESULTS: This study included 7922 patients, of whom 3498 were transfused and 4424 were not transfused. In the propensity-weighted cohort, the mean nadir Hb for each group was 7.75 g/dL. The 30-day cumulative incidence rate for the primary outcome after application of propensity score weights was 28.6% in the transfusion group and 33.3% in the no-transfusion group (hazard ratio [HR], 0.83, 95% confidence interval [CI], 0.75-0.91), which persisted at 1 year after hospital discharge and across sensitivity analyses. CONCLUSIONS: In patients hospitalized for ACS who experience nadir Hb levels between 6.0 and 8.9g/dL, RBC transfusion was associated with a reduction in the composite event of all-cause death and hospitalization for MI within 30-days after 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".