The impact of increasing the upper age limit of donation on the eligible blood donor population in Canada
Bibliographic record
Abstract
OBJECTIVES: Using population prevalence data for deferrable diseases/conditions we estimated the Canadian population eligible to donate according to three upper-age limit scenarios. BACKGROUND: The donor selection criteria limit the number of potential blood donors but relaxing the upper age criteria could mitigate this. METHODS AND MATERIALS: Forty deferral criteria were identified and their corresponding prevalence data obtained to estimate the number of people excluded by the criteria. The eligible blood donor population was estimated from national census data taking the age limits, deferral criteria and deferral time-period into account. As more than one disease/condition may co-exist, the estimate was adjusted to avoid over-representation. RESULTS: Of about 33 million Canadians aged 17 (18 in Québec) to 65, 15·1 million (45·8%) are eligible to donate blood. This number increases to 15·7 million when including people up to 71 years and to 17·1 million in the absence of an upper age limit. CONCLUSION: As about 1·2 million units are collected from 600,000 donors annually, there are more than enough eligible people to meet the need. However, recruitment of donors is challenging and the absence of an upper age limit allows an additional 2 million people to donate. Other countries may wish to consider modification of the upper age criterion to address the effect of an aging population on the blood supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".