Evolving policies for donors with diabetes: The Canadian experience
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Donor criteria for diabetes vary significantly. We describe our evolving policies for donors with diabetes, their contribution to the Canadian blood supply and their rate of syncopal reactions compared to other donors. MATERIALS AND METHODS: All donors are asked if they have diabetes and have taken medications in the last 3 days. We assessed donors with diabetes on various medications, the number deferred over time, and syncopal reactions in donors with diabetes and other donors in our donor reaction database. RESULTS: Policy changes allowing type 2 diabetic donors on oral hypoglycaemics alone, type 2 diabetic donors on oral medications and insulin and type 1 diabetic donors (all on insulin) to donate resulted in a decrease in deferrals from 450 to 22 donors annually. Of donors being treated with medication for diabetes, 11% are receiving insulin as part of their treatment. Syncopal reaction rates were low and not statistically different between diabetic and non-diabetic donors, although confidence intervals (CIs) are large. CONCLUSION: Policies decreased deferrals while maintaining safety. A longer observation period would strengthen these observations.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".