An Intraoral Closed-Loop Monitoring and Stimulation System for Treatment of Swallowing Problems
Bibliographic record
Abstract
One in every 25 Americans suffer from swallowing disorders, referred to as Dysphagia. Problems in the pharyngeal phase of swallowing are hard to treat because of the neuromuscular complexity in the region and the quick passage of food (ndmolar, potentially lesser palatine nerve, as a closed-loop. We hypothesize that the closed-loop stimulation on the soft palatal area inside the 2ndmolar, timed with the onset of the pharyngeal swallowing, augments the sensory feedback and promotes triggering of the pharyngeal swallowing phase. Two experiments were performed to test the hypothesis. In the first experiment, the swallowing time and acceleration of laryngeal excursion was recorded without any stimulation. In the second experiment, stimulation was provided to the lesser palatine nerve for 500 ms when tongue tip was detached from the incisors, and the swallowing time and acceleration of laryngeal excursion was recorded. Two human subjects participated in the study. Without stimulation, both subjects showed consistent swallowing in both duration and amplitude. Stimulation reduced the peak-to-peak duration of laryngeal excursion, but the peak-to-peak amplitude of laryngeal excursion was not changed by stimulation. This study found that closed-loop stimulation onto the palatal area inside the 2ndmolar, timed with the onset of the pharyngeal swallowing, can reduce the duration of the pharyngeal swallowing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".