Mechanisms of “Cough-Trick” for Pain Relief during Venipuncture: An Experimental Crossover Investigation in Healthy Volunteers
Bibliographic record
Abstract
Objectives. The easily performed “cough-trick” (CT) reduces pain during venipuncture (VP), although the underlying mechanism remains unclear. The aim was to investigate the pain-reducing effect of CT during VP in comparison with two distraction methods, as well as under the influence of naloxone.Methods. 54 healthy male volunteers participated in 3 investigations. Pain during standardized VP with CT was compared to a “weak” distraction (squeezing a rubber ball; investigation 1; n=20 ) and to a “strong” distraction (inflating a tourniquet to a given pressure; investigation 2; n=21 ). In investigation 3 ( n=13 ), pain at a VP without intervention was compared to pain at VP with CT under naloxone; pressure pain thresholds before and after naloxone administration were also measured. Pain was assessed using a 100 mm visual analogue scale. Data were compared within each sample using Student’st-test for paired samples.Results. Pain intensity at VP with CT was lower than under “weak” distraction (mean difference 5 mm; 95% CI: 0.5 to 9.6; P=0.03 ). Pain levels under CT and “strong” distraction were comparable. There was no difference between pain under CT after naloxone infusion and pain without intervention. Pressure pain threshold decreased (mean difference 1 mm; 95% CI: 0.1 to 1.0 mm; P=0.02 ) after naloxone administration.Conclusion. Pain-reducing effect of CT during VP is superior to that of simple motor distraction and equivalent to a complex distraction method. This might be due to the activation of segmental pain inhibitory pathways during coughing indicated through the lack of pain reduction due to CT under opioid antagonist blockage.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".