Optimal Approaches for Measuring Tongue-Pressure Functional Reserve
Bibliographic record
Abstract
Tongue-palate pressure is a parameter of considerable interest in the field of dysphagia. Maximum isometric tongue-palate pressures (MIPs) decline in healthy aging and in dysphagia. Functional reserve (FR) is the difference between MIPs and swallowing pressures. Reduced FR is thought to constitute a risk for developing functional swallowing impairments. We compare different approaches for calculating FR and recommend an optimal approach. Tongue-palate pressure data were collected from 78 healthy adults (40 < age 40; 38 > 60) during anterior and posterior MIPs, regular (RESS) and effortful (ESS) saliva swallows, and water swallows (4 repetitions per task). Six different measures of reserve were calculated using maximum anterior MIPs or ESS pressures at the top, and mean or maximum RESS or water swallow pressures at the bottom of the range. Correlations with age and MIPs were explored to confirm suitability for measuring FR. The impact of normalization to maximum MIP range was explored. We conclude that an optimal measure of FR involves the comparison of maximum MIP with mean saliva swallowing pressures. This parameter declines with age, but when normalized to an individual's MIP range, the relationship is no longer evident. This suggests that FR does not necessarily decline in healthy aging.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".