An international investigation into O red blood cell unit administration in hospitals: the GRoup O Utilization Patterns (GROUP) study
Bibliographic record
Abstract
BACKGROUND: Transfusion of group O blood to non-O recipients, or transfusion of D- blood to D+ recipients, can result in shortages of group O or D- blood, respectively. This study investigated RBC utilization patterns at hospitals around the world and explored the context and policies that guide ABO blood group and D type selection practices. STUDY DESIGN AND METHODS: This was a retrospective study on transfusion data from the 2013 calendar year. This study included a survey component that asked about hospital RBC selection and transfusion practices and a data collection component where participants submitted information on RBC unit disposition including blood group and D type of unit and recipient. Units administered to recipients of unknown ABO or D group were excluded. RESULTS: Thirty-eight hospitals in 11 countries responded to the survey, 30 of which provided specific RBC unit disposition data. Overall, 11.1% (21,235/191,397) of group O units were transfused to non-O recipients; 22.6% (8777/38,911) of group O D- RBC units were transfused to O D+ recipients, and 43.2% (16,800/38,911) of group O D- RBC units were transfused to recipients that were not group O D-. Disposition of units and hospital transfusion policy varied within and across hospitals of different sizes, with transfusion of group O D- units to non-group O D- patients ranging from 0% to 33%. CONCLUSION: A significant proportion of group O and D- RBC units were transfused to compatible, nonidentical recipients, although the frequency of this practice varied across sites.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".