Influence of the Perceived Taste Intensity of Chemesthetic Stimuli on Swallowing Parameters Given Age and Genetic Taste Differences in Healthy Adult Women
Bibliographic record
Abstract
PURPOSE: This study examined whether the perceived taste intensity of liquids with chemesthetic properties influenced lingua-palatal pressures and submental surface electromyography (sEMG) in swallowing, compared with water. METHOD: Swallowing was studied in 80 healthy women, stratified by age group and genetic taste status. General Labeled Magnitude Scale ratings of taste intensity were collected for deionized water; carbonated water; 2.7% w/v citric acid; and diluted ethanol. These stimuli were swallowed, with measurement of tongue-palate pressures and submental sEMG. Path analysis differentiated stimulus, genetic taste status, age, and perceived taste intensity effects on swallowing. Signal amplitude during effortful saliva swallowing served as a covariate representing participant strength. RESULTS: Significant differences (p < .05) in taste intensity were seen across liquids: citric acid > ethanol > carbonated water > water. Supertasters perceived greater taste intensity than did nontasters. Lingua-palatal pressure and sEMG amplitudes were correlated with the strength covariate. Anterior palate pressures and sEMG amplitudes were significantly higher for the citric acid stimulus. Perceived taste intensity was a significant mediator of stimulus differences. CONCLUSION: These data provide confirmatory evidence that high-intensity sour stimuli do influence swallowing behaviors. In addition, taste genetics influence the perception of taste intensity for stimuli with chemesthetic properties, which modulates behavioral responses.
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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 | 0.002 |
| 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) | 0.002 | 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".