Group O <scp>RBC</scp>s: where is universal donor blood being used
Bibliographic record
Abstract
BACKGROUND: There have been recurrent shortages of group O blood due to insufficient inventory and use of group O blood in ABO non-identical recipients. We performed a 12-year retrospective study to determine utilization of group O Rh-positive and Rh-negative red blood cells (RBCs) by recipient ABO group. Reasons for transfusing group O blood to ABO non-identical recipients were also assessed. METHODS: Utilization data from all group O Rh-positive and Rh-negative RBCs transfused at three academic hospitals between April 2002 and March 2014 were included. Data were extracted from Transfusion Registry for Utilization Surveillance and Tracking, a comprehensive database with inventory information on all blood products received at the hospitals. Extracted data included product type, ABO and Rh, final disposition (transfused, wasted, outdated), and demographic and clinical data on all patients admitted to hospital. Descriptive statistics were performed using sas 9.3. RESULTS: There were 314 968 RBC transfusions: 151 645 (48·1%) were group O, of which 138 136 (91·1%) RBC units were transfused to group O individuals. ABO non-identical recipients received 13 509 group O RBCs (8·9%). The percentage of group O RBCs transfused to ABO non-identical recipients by fiscal year varied from 7·8% to 11·1% with a steady increase from 2011 to 2013. Reasons for this included: trauma, outdating, outpatient usage and shortages. CONCLUSION: The practice of transfusing O RBCs to non-O individuals has been increasing. Specific hospital and blood supplier policies could be targeted to change practice, leading to a more sustainable group O red blood cell supply.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".