The impact of age and sex on first‐time donor return behavior
Bibliographic record
Abstract
BACKGROUND: This study examined the impact of age and sex of first-time donors who had not experienced an adverse event or deferral on their likelihood of and time to return. STUDY DESIGN AND METHODS: On behalf of the Biomedical Excellence for Safer Transfusion (BEST) Collaborative, international blood collection agencies (BCAs) were invited to provide data on first-time whole blood donors in 2014, including initial presentation date, collection site type, age, sex, blood type, return to donate within 24 months (yes/no), and subsequent presentation date. RESULTS: Eight BCAs contributed 706,789 records. The overall odds of returning to donate were slightly lower in female versus male donors, and the overall age trend was U-shaped with younger and older donors having higher odds for returning relative to middle-aged donors. However, variations by BCA were observed. Specifically, in three BCAs, women had higher odds of returning to donate than men. Further, while across seven BCAs the smallest cohort of older first-time donors returned at a higher rate and returned more quickly than middle-aged first-time donors, the behavior of younger donors varied substantially between BCAs. CONCLUSION: While older first-time donors are more likely to return and return more quickly than middle-aged donors they make up only a small proportion of first-time donors, whereas the larger group of younger donors exhibits less clear patterns of return compared to middle-aged donors. Further research is needed to determine whether targeting the recruitment of older donors or bolstering retention of middle-aged donors would be most effective in maintaining 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".