Variability and predictability of large‐volume red blood cell transfusion in cardiac surgery: a multicenter study
Bibliographic record
Abstract
BACKGROUND: In cardiac surgery, excessive blood loss requiring large-volume red blood cell (RBC) transfusion is a common occurrence that is associated with significant morbidity and mortality. The objectives of this study were to measure the interinstitution variation and predictability of large-volume RBC transfusion. STUDY DESIGN AND METHODS: Data were retrospectively collected on 3500 consecutive cardiac surgical patients at seven Canadian hospitals during 2004. The crude and risk-adjusted institutional odds ratios (ORs) for large-volume (>or=5 U) RBC transfusion were calculated with logistic regression. The predictive accuracy of an existing prediction rule for large-volume RBC transfusion was calculated for each institution. RESULTS: Large-volume RBC transfusion occurred in 538 (15%) patients. When compared to the reference hospital (median crude rate), the institutional unadjusted and adjusted ORs for large-volume RBC transfusion ranged from 0.29 to 1.26 and 0.14 to 1.15, respectively (p<0.0001 for interinstitution variation). The variation was lower, but still considerable, for excessive blood loss, defined as at least 5-U RBC transfusion or reexploration; the ORs ranged from 0.42 to 1.22 (p<0.0001). The prediction rule performed well at most sites; its pooled positive predictive value for excessive blood loss was 71 percent (range, 63%-89%), and its negative predictive value was 90 percent (range, 87%-93%). CONCLUSIONS: There is marked interinstitution variation in large-volume RBC transfusion in cardiac surgery that is not explained by patient- or surgery-related factors. Despite this variation, patients at high or low risk for large-volume RBC transfusion can be accurately identified by a prediction rule composed of readily available clinical variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".