Moving from a permanent to a 5 year deferral for donors with cured cancer results in a substantial reduction in deferral rates
Bibliographic record
Abstract
Background and Objectives Donor eligibility policies can be precautionary and restrictive, resulting in substantial donation loss. Canadian Blood Services changed from a permanent to a five‐year deferral for most non‐cutaneous cancers following a recently published large‐scale Scandinavian data linkage study. We evaluated the impact of this change on deferral rates. Materials and Methods We assessed the number and rate of deferrals for cancer in the two years before and after the criteria change, and examined computer records of 100 consecutive donors with a history of cancer. Results Overall deferrals declined from 3.1 to 1.1 per 1000 donations. The reduction was similar for males and females, but greater for first time donors (9.4 to 3.2/1000) than repeat donors (2.4 to 0.8/1000). Conclusions Deferrals were substantively reduced, with the greatest impact on new donors. Studies performed in one jurisdiction may benefit donors and ultimately patients in other countries.
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 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".