Respiration and applied tension strategies to reduce vasovagal reactions to blood donation: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND Whether produced by breathing too fast or too deeply, hyperventilation is common in stressful situations and may contribute to blood donation–related vasovagal symptoms. The effects of some previously tested interventions for vasovagal symptoms, for example, applied tension (AT), may be related to reduction of hyperventilation. More targeted breathing techniques might be useful. STUDY DESIGN AND METHODS This was a randomized controlled trial comparing the effects of AT, a slow, shallow “anti‐hyperventilation” breathing technique previously tested in phobic individuals (respiration control [RESP]), the combination of AT and RESP, and no intervention on blood donors participating in university clinics. A total of 547 eligible donors were assigned randomly to one of these four groups. Observational, self‐report, and physiologic measures (primarily via respiratory capnometry) were obtained. RESULTS Although both RESP and AT had some positive impact on blood donation outcome, the effects of RESP were more numerous, albeit limited primarily to donors who had less general fear of medical procedures. For example, lower‐fear donors assigned to practice RESP had significantly lower Blood Donation Reaction Inventory scores and were significantly less likely to require treatment for symptoms than no‐treatment individuals. In general, RESP led to a significant decrease in respiration rate, though it did not influence end‐tidal CO2, a more precise measure of hyperventilation. CONCLUSION While the mechanisms remain somewhat unclear and the interventions did not benefit more fearful, higher‐risk donors, respiration control is a promising additional approach to reducing vasovagal symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".