Tongue Movements During Water Swallowing in Healthy Young and Older Adults
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore the nature and extent of variability in tongue movement during healthy swallowing as a function of aging and gender. In addition, changes were quantified in healthy tongue movements in response to specific differences in the nature of the swallowing task (discrete vs. sequential swallows). METHOD: Electromagnetic midsagittal articulography (EMMA) was used to study the swallowing-related movements of markers located in midline on the anterior (blade), middle (body), and posterior (dorsum) tongue in a sample of 34 healthy adults in 2 age groups (under vs. over 50 years of age). Participants performed a series of reiterated water swallows, in either a discrete or a sequential manner. RESULTS: This study shows that age-related changes in tongue movements during swallowing are restricted to the domain of movement duration. The authors confirm that different tongue regions can be selectively modulated during swallowing tasks and that both functional and anatomical constraints influence the manner in which tongue movement modulation occurs. Sequential swallowing, in comparison to discrete swallowing, elicits simplification or down-scaling of several kinematic parameters. CONCLUSION: The data illustrate task-specific stereotyped patterns of tongue movement in swallowing, which are robust to the effects of healthy aging in all aspects other than movement duration.
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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) | 0.001 | 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".