Dismantling applied tension: mechanisms of a treatment to reduce blood donation–related symptoms
Bibliographic record
Abstract
BACKGROUND: Blood donation-related symptoms such as dizziness, nausea, and fainting are unpleasant for the donor and a significant disincentive for repeat donation. The muscle tensing technique of applied tension (AT) reduced symptoms in several studies. STUDY DESIGN AND METHODS: This study was a randomized controlled trial of different components of AT. A total of 1209 donors were randomly assigned to one of six conditions involving tension of different muscle groups or donation as usual. Dependent measures included a symptom questionnaire and whether or not the donor's chair was reclined to treat a reaction. RESULTS: Replicating previous findings, donors who practiced the "full" AT procedure reported significantly fewer symptoms, were less likely to require chair reclining, and rated their chances of giving blood again as greater than those in the donation-as-usual group. Of the component groups, donors who tensed only their lower body were most similar to the full-AT group. Upper-body tension in and of itself did not reduce symptoms though another condition involving upper body tension, which directed attention away from the arm with the needle in it had several significant effects. CONCLUSION: The positive effects of AT on blood donation outcome appear to be mediated primarily by lower-body tension though distraction also probably contributes to its impact.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".