Medication use in Canadian blood donors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Policies regarding medication use vary between blood centres. We evaluated medication use in eligible Canadian Blood Services whole blood donors to inform possible process improvements and allow comparisons between donors and the general population. MATERIALS AND METHODS: All donors are asked about medication use in the last 3 days, and medications and their reason for use are documented in our donor computer system. Donor computer records were reviewed from January 1, 2020, to March 31, 2022 to extract information on medications by donor age and sex; medications were grouped into therapeutic classes. Stability of medication use over time was determined in a random sample of 100 donors who made at least two donations in the study period. RESULTS: One-third of successful (eligible) donors were taking medications; of these, 80% were on one or two medications. Five classes of medication accounted for 72% of medication use, and 13 classes account for 93% of use. Use remains relatively stable over time. CONCLUSION: Medication use is common, with a few classes accounting for most use. Drop-down lists and storage of information from one donation to the next may enhance efficiency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".