Trends in age and red blood cell donation habits among several racial/ethnic minority groups in the United States
Bibliographic record
Abstract
BACKGROUND: To meet the needs of a diverse patient population, an adequate supply of red blood cells (RBCs) from ethnic/racial minority donors is essential. We previously described the 10-year changes in minority blood donation in the United States. This study describes donation patterns by donor status, age, and race/ethnicity. STUDY DESIGN AND METHODS: Data on the age and the number of unique black/African American, Hispanic/Latino, Asian, and white RBC donors were obtained from eight US blood collectors for 2006, 2009, 2012, and 2015. Donors self-identified their race/ethnicity. First-time (FT) and repeat (R) donors were analyzed separately. RESULTS: Overall, for both FT and R donor groups, whites constituted the majority of unique donors (FT 66.7% and R 82.7%) and also donated the greatest proportion of RBC units (FT 66.6% and R 83.8%). Donors less than 20 years old comprised the greatest proportion of FT donors for all racial/ethnic groups (39.2%) and had the highest mean number of RBC donations per donor (1.12) among FT donors. Conversely, R donors less than 20 years old had some of the lowest mean number of RBC donations per donor (1.55) among R donors, whereas R donors at least 60 years old had the highest mean (1.88). Year by year, the percentage of FT donors who were less than 20 years old increased for all race/ethnicities. For R donors, whites were more frequently older, while Hispanics/Latinos and Asians were younger. CONCLUSIONS: Greater efforts to convert FT donors less than 20 years into R donors should be undertaken to ensure the continued diversity of the blood supply.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".