Bibliographic record
Abstract
In this paper, a mathematical modeling of the swallowing sound generation is presented. To evaluate the model, its application on swallowing disorder (dysphagia) diagnosis is discussed. As a starting point, a simple linear time invariant model is assumed to represent the pharyngeal wall and tissue excited by a train of impulses. The modeling is approached by two different assumptions. In one approach, it is assumed that the impulse train, representing the neural activities to trigger swallow, is the same for both groups of control and dysphagic, and it is the pharyngeal model that accounts for the difference between the two groups. On the other hand, in the second approach, it is assumed that the pharyngeal response is the same for both groups, but the neural activities to initiate the swallow are different between the two groups. The results show that the second approach complies better with the physiological characteristics of swallowing mechanism as it provides a much better discrimination between the swallowing sounds of control and dysphagic groups of this study. Though, it should be noted that our dysphagic group subjects were cerebral palsy and stroke patients. Hence, the model accounting for initiation of neural activities is reasonable to show better results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".