Modulation of Tongue Pressure According to Liquid Flow Properties in Healthy Swallowing
Bibliographic record
Abstract
Purpose During swallowing, the tongue generates the primary propulsive forces that transport material through the oral cavity toward the pharynx. Previous literature suggests that higher tongue pressure amplitudes are generated for extremely thick liquids compared with thin liquids. The purpose of this study was to collect detailed information about the modulation of tongue pressure amplitude and timing across the range from thin to moderately thick liquids. Method Tongue pressure patterns were measured in 38 healthy adults (aged under 60 years) during swallowing with 4 levels of progressively thicker liquid consistency (International Dysphagia Diet Standardisation Initiative, Levels 0 = thin, 1 = slightly thick, 2 = mildly thick, and 3 = moderately thick). Stimuli with matching gravity flow (measured using the International Dysphagia Diet Standardisation Initiative Flow Test; Cichero et al., 2017 ; Hanson, 2016 ) were prepared both with/without barium (20% weight per volume concentration) and thickened with starch and xanthan gum thickeners. Results After controlling for variations in sip volume, thicker liquids were found to elicit significantly higher amplitudes of peak tongue pressure and a pattern of higher (i.e., steeper) pressure rise and decay slopes (change in pressure per unit time). Explorations across stimuli with similar flow but prepared with different thickeners and with/without barium revealed very few differences in tongue pressure, with the exception of significantly higher pressure amplitudes and rise slopes for nonbarium, starch-thickened slightly and mildly thick liquids. Conclusions There was no evidence that the addition of barium led to systematic differences in tongue pressure parameters across liquids with closely matched gravity flow. Additionally, no significant differences in tongue pressure parameters were found across thickening agents. Supplemental Material https://doi.org/10.23641/asha.7616537.
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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 | 0.001 | 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) | 0.001 | 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".