Evaluating appropriate red blood cell transfusions: a quality audit at 10 Ontario hospitals to determine the optimal measure for assessing appropriateness
Bibliographic record
Abstract
BACKGROUND: Evaluating the appropriateness of red blood cell (RBC) transfusion requires labor-intensive medical chart audits and expert adjudication. We sought to determine the appropriateness of RBC transfusions at 10 hospitals using retrospective chart review and to determine whether simple metrics (proportion of single-unit transfusions, RBCs/100 acute inpatient days, proportion of transfusions with pretransfusion hemoglobin <80 g/L or posttransfusion hemoglobin <90 g/L) could be used as surrogate markers of appropriateness by comparing their values with the results from the audit. STUDY DESIGN AND METHODS: An initial block of 30 RBC units was dually adjudicated for appropriateness followed by additional blocks of 10 units until the difference between the cumulative percentage of appropriate RBC units in the preceding block and final block was <3%. Pearson correlation tests were used to evaluate associations between the metrics and percentages of appropriate transfusions per hospital. Two-by-two tables were used to assess the utility of the metrics to classify transfusions for appropriateness. RESULTS: Of the 498 units audited, 78% were adjudicated as appropriate (κ = 0.9603), with significant variability between institutions (p < 0.0001). Fifty audits or less were required at nine of the institutions. The values of the metrics were not found to have significant correlations with appropriateness, and the metric that misclassified the smallest proportion of transfusions for appropriateness was pretransfusion hemoglobin <80 g/L, at 24%. CONCLUSIONS: Our findings suggest that a chart audit of 50 RBC transfusions with adjudication using robust criteria is the optimal means of evaluating RBC transfusion appropriateness at an institution for benchmarking and quality-improvement initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".