Trends in <scp>US</scp> minority red blood cell unit donations
Bibliographic record
Abstract
BACKGROUND: To provide the appropriately diverse blood supply necessary to support alloimmunized and chronically transfused patients, minority donation recruitment programs have been implemented. This study investigated temporal changes in minority red blood cell (RBC) donation patterns in the United States. STUDY DESIGN AND METHODS: Data on donor race and ethnicity from 2006 through 2015, including the number of unique donors, collections, RBCs successfully donated, and average annual number of RBC donations per donor (donor fraction), were collected from eight US blood collectors. Minority donors were stratified into the following groups: Asian, black or African American, Hispanic or Latino, Native Indian or Alaska Native, Native Hawaiian or other Pacific Islander, white, multiracial/other, and no answer/not sure. RESULTS: Over the 10-year period, white donors annually constituted the majority of unique donors (range, 70.7%-73.9%), had the greatest proportion of collections (range, 76.1%-79.8%), and donated the greatest proportion of RBC units (range, 76.3%-80.2%). These donors also had the highest annual donor fraction (range, 1.82-1.91 units per donor). Black or African American donors annually constituted between 4.9 and 5.2% of all donors during the study period and donated between 4.0 and 4.3% of all RBC units. Linear regression analysis revealed decreasing numbers of donors, collections, and donated RBC units from white donors over time. CONCLUSION: Although the US population has diversified, and minority recruitment programs have been implemented, white donors constitute the majority of RBC donors and donations. Focused and effective efforts are needed to increase the proportion of minority donors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".