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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".