Implementation of measures to reduce vasovagal reactions: Donor participation and results
Bibliographic record
Abstract
BACKGROUND: There are several strategies to reduce donor reactions. We report donor participation and reaction rates before and after implementing multiple measures at Canadian Blood Services. STUDY DESIGN AND METHODS: We introduced a structured program of 500 mL of water and a salty snack pre-donation and applied muscle tension (AMT) during donation. Donors were not deferred for out of range blood pressure (BP); however, BP was measured in first time donors. Time on the donation chair post-donation was decreased from 5 to 2 min for repeat donors. We assessed participation rates using our quarterly survey of 10,000 recent donors. We extracted vasovagal reactions with loss of consciousness (LOC) from our operational database and compared pre-implementation (Oct 12,018-March 31,2019) and post-implementation (Oct 12,019-March 31,2020) periods. RESULTS: Survey response rates varied from 11% to 16%. The percentage of donors who drank the water and ate the salty snack increased from 58% to 82% and 44% to 70% over 4 quarters; those performing AMT increased from 24% to 41%. Reactions decreased from 19.07 per 10,000 (744 reactions in 390,123 donations) to 14.04 per 10,000 (537 in 382,382 donations) (p < .0001). No first-time donors with high BP (n = 684) but 5 with low BP (n = 718) had reactions, CI were very large. CONCLUSIONS: Achieving optimal participation was challenging. After implementation of a donor wellness initiative based on best practice, rates of vasovagal reactions with LOC decreased by 25%. A larger dataset is necessary to assess the safety contribution of BP deferrals when other mitigation measures are in place.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".