Pre‐donation water and salty snacks to prevent vasovagal reactions among blood donors
Bibliographic record
Abstract
BACKGROUND: Minimizing the risk of vasovagal reactions (VVRs) can prevent donor harms and improve donor return. We report the results of a program to reduce VVR rates. STUDY DESIGN AND METHODS: The program was implemented on June 11, 2017 and consisted in drinking water and eating a salty snack before donating blood, plasma, or platelets. All donations made during the "pre-program period" (October 11, 2015-June 10, 2017) and "post-program period" (June 11, 2017-May 11, 2019) were included. Study outcomes comprised VVRs (any severity) and syncopal VVRs, whether employee- or donor-reported. An interrupted time series (ITS) analysis proxied causality based on the "pre-program trend," the "immediate trend" (i.e., immediately before versus after the program), and the "post-program trend". The relative risk (RR) of VVR (along with confidence intervals [CIs]) was reported, overall and stratified by subgroups based on age, sex, donor type (i.e., first-time versus repeat), and donation type (i.e., whole blood versus apheresis). RESULTS: The monthly VVR rate (any severity) dropped from 4.6% in the pre-program period to 4.3% in the post-program period, and never reached its pre-program level. The ITS analysis revealed a statistically significant and increasing pre-program trend (RR [95% CI] = 1.011 [1.002-1.020]), a statistically significant and decreasing immediate trend (RR [95% CI] = 0.848 [0.743-0.969]), and a non-statistically-significant and stable post-program trend (RR [95% CI] = 0.999 [0.993-1.006]). Similar trends were observed for nearly all high- and low-risk subgroups. No statistically significant trend was observed for syncopal VVRs. DISCUSSION: These results suggest that the herein-described program durably reduced the incidence of VVRs (any severity) by ~15%.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".