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Record W1998351890 · doi:10.1016/s0304-3959(03)00333-6

Sweet taste and blood pressure-related analgesia

2003· article· en· W1998351890 on OpenAlexaff
Maxim Lewkowski, Blaine Ditto, Marios Roussos, Simon N. Young

Bibliographic record

VenuePain · 2003
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsTastePain toleranceBlood pressureSweet tasteAnalgesicAnesthesiaMedicineStimulus (psychology)OpioidThreshold of painInternal medicinePsychologyFood scienceChemistryReceptor

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.235
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2003
Admission routes1
Has abstractyes

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