A New, Portable Orofacial Manometer for Measuring Tongue Strength and Endurance in Children: Laboratory-Based Validity Study
Bibliographic record
Abstract
BACKGROUND: An accurate tongue strength and endurance assessment is necessary for pediatric dysphagia. TongueFit is a new portable orofacial manometer for measuring tongue strength and endurance and a game-based training app for children. OBJECTIVE: This study tests the validity of TongueFit compared to the standard manometer as the current gold standard for measuring air pressure. METHODS: This laboratory study compared TongueFit and a standard manometer as the gold standard for measuring air pressure. This study was conducted in 3 different experimental conditions. The first experiment compared TongueFit and the standard manometer using Force Tester (MCT-2150) and pressure controlled by MSatLite software. The second and third experiments involved 2 cm and 3 cm bulbs between the two devices. This study used Lin's concordance correlation to measure the level of agreement. RESULTS: There was a mean absolute difference of 0.005 kPa between the TongueFit and the standard manometer (n=35, ρC=1.00). Statistical analysis shows perfect agreement correlation (ρC =1.000). By using the 2 cm bulb, TongueFit's mean is 0.007 kPa lower, also showing perfect agreement (ρC = 1.000). Moreover, using the 3 cm bulb, results show almost perfect agreement (ρC =0.999) with the TongueFit's mean 0.044 kPa lower. CONCLUSIONS: This study confirms the high validity of TongueFit as an orofacial manometer compared to the standard manometer, with negligible mean differences, near perfect and perfect agreement in the experiments. These results confirm that TongueFit is a valid and accurate tool for assessing tongue strength.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".