Opportunities for improving platelet transfusion practice: A large retrospective audit across 22 hospitals
Bibliographic record
Abstract
Summary Despite evidence‐based guidelines to inform platelet transfusion practice, unnecessary platelet transfusion persists. We performed a multicentre retrospective analysis of adults admitted to general medicine, subspecialty medicine and critical care from 1 January 2017 to 30 June 2022. Platelet transfusion guideline compliance was defined as platelet transfusion below 100 × 10 9 /L for neurosurgical, cardiac surgery or extracorporeal membrane oxygenation indications, below 50 × 10 9 /L for invasive procedures, bleeding or therapeutic anticoagulation, and below 10 × 10 9 /L if the patient did not have an immune‐mediated thrombocytopenia. We analysed 821 950 patient admissions at 22 hospital sites, identifying 56 825 platelet transfusion events. Overall, 13 199 (23.2%) platelet transfusion events were guideline‐non‐compliant. High rates of non‐compliant transfusions were observed in the context of anti‐platelet therapy ( n = 1515, 48.5% non‐compliant), cardiac surgery ( n = 1935, 49.7%), invasive procedures ( n = 4648, 29.2%), immune‐mediated thrombocytopenia ( n = 596, 32.9%) and primary prophylaxis ( n = 7370, 47.2%). After adjusting for physician characteristics, there was a lower risk of guideline‐non‐compliant platelet transfusions at academic than at community hospitals (odds ratio [OR] 0.768, 95% confidence interval [CI] 0.678–0.871, p < 0.001). Physician specialty, but not physician gender or years in practice, influenced guideline compliance. These findings underscore the need for targeted intervention to optimize platelet transfusion practices, minimize avoidable transfusion reactions, reduce costs and mitigate platelet shortages.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".