Bibliographic record
Abstract
Background The upper age limit for blood donation differs in various countries, due to concerns about donor safety. In recent years, many countries have reviewed their policies regarding the upper age limits for donation. Evaluation of the impact of changing population and donor demographics may assist in better planning to meet future patient needs for blood. Aims We review current policies, evidence of the safety of donation in older individuals, and the contribution that older donors can make to the blood supply. Methods We reviewed policies in countries that are part of the Biomedical Excellence for Safer Transfusion Collaborative, and published studies on the safety of donation. We evaluated the contribution that older donors make to the blood supply at Canadian Blood Services. Results The upper age limit for regular whole blood donors ranges from 69–70 to 80; blood centres in several countries have recently removed the upper age limit, while many blood centres in the US have long had no upper age limit for donation. Data demonstrate that healthy older individuals can safely donate. Approximately 1·5% of all whole blood donations at Canadian Blood Services are made by donors past their 71st birthday. Summary/Conclusions There are more healthy older individuals than ever before. Many donors would like to continue donating into their eighth decade, and perhaps beyond. There is increasing evidence that donation is safe in older individuals, and that they can make a significant contribution to 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".