Sweet taste and blood pressure-related analgesia
Bibliographic record
Abstract
To test the prediction that sweet taste modifies responses to cold induced pain, 72 young adults held sweet, bitter and water solutions in their mouths, in counterbalanced order, before and during a cold pain stimulus. To test whether or not blood pressure interacts with sweet taste analgesia, measurements of resting blood pressure were also obtained. A significant main effect of taste on pain tolerance was observed, as well as a significant interaction between resting mean arterial pressure (MAP) and taste on tolerance. Sweet taste was associated with a prolongation of tolerance compared to the bitter and water conditions. When participants were split along the median for MAP, sweet taste was associated with an 18.1% increase in pain tolerance compared with water for those with lower MAP. No significant impact of taste on pain sensitivity was observed among participants with higher MAP. Groupwise comparisons revealed a significant difference in pain tolerance between participants with higher and lower MAP in the water condition but not in the sweet condition, replicating previous findings of a reduced sensitivity to pain among those with higher blood pressure. The analgesic effects of sweet tasting solutions seen previously in human infants and children may also be present in adults. Individuals with higher blood pressure may not be as sensitive to the presumably opioid-mediated analgesic effects of sweet taste, perhaps due to opioid dysregulation.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".