Assessing the efficacy of a single‐unit red blood cell transfusion policy at a multisite transfusion service using a computerized retrospective audit
Bibliographic record
Abstract
Background and Objectives In 2013, an Eastern Canada blood bank implemented a new policy to reduce red cell transfusion. The policy mandated clinical or laboratory reassessment of stable, non‐bleeding patients after each unit transfused, before issuing subsequent units. A computerized audit assessed the policy's effectiveness. Materials and Methods This retrospective cohort study compares the policy's effect on transfusion practice across three groups of adult inpatients: haematology, surgery and internal medicine. Compliance was inferred from increases in the proportion of single‐unit red cell transfusions among all single‐ and double‐unit transfusions. Outcome variables included transfusion intensity (red cell units administered per admission involving a transfusion) and pretransfusion haemoglobin levels. Results Each group had more transfusions issued as single units during the ten months following policy enforcement. In haematology patients, single‐unit transfusions increased from 17% to 89% and transfusion intensity decreased (median: 2–2, Q1: 2–1, Q3: 6–4, P < 0·001). Single‐unit transfusions increased from 57% to 94% in medicine patients and from 63% to 87% in surgery patients. Transfusion intensity also decreased in surgical patients (median: 2–1, Q1: 1–1, Q3: 2–2, P < 0·001) and in medicine patients (median: 2–1, Q1: 1–1, Q3: 2–2, P = 0·008). No group showed a clinically significant change in pretransfusion haemoglobin levels. Conclusion The audit demonstrated significant compliance with a single‐unit transfusion policy. Transfusion intensity decreased in all groups despite no clinically significant change in pretransfusion haemoglobin levels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".