Tongue Pressure and Submental Surface Electromyography Measures During Noneffortful and Effortful Saliva Swallows in Healthy Women
Bibliographic record
Abstract
PURPOSE: The effortful swallow, a compensatory technique frequently employed by speech-language pathologists for their patients with dysphagia, is still not fully understood in terms of how it modifies the swallow. In particular, although age-related changes are known to reduce maximum isometric tongue pressure, it is not known whether age affects people's ability to perform the effortful swallow. In this study, differences were explored between younger and older healthy women in execution of the effortful swallowing maneuver through a comparative analysis of effortful and noneffortful swallows. METHOD: Eighty healthy women (40 age 18-35 years and 40 age 60 and older) participated. Peak amplitude measures and the timing of signal onset to peak were measured using concurrent tongue pressure and submental surface electromyography. Result Statistically significant main effects of age group were not observed in the amplitude data, but older participants showed slower rise times to peak anterior tongue-palate pressure. CONCLUSIONS: Despite the general age-related deterioration of the swallowing musculature due to the phenomenon of sarcopenia, older women can still produce noneffortful and effortful swallows with lingual pressure and submental surface electromyography amplitudes similar to younger women.
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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) | 0.001 | 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".