Kangaroo Stimulation Game in Tracheostomized Intensive Care–Related Dysphagia: Interventional Feasibility Study
Bibliographic record
Abstract
Background: Dysphagia is common in intensive care unit (ICU) patients. Using surface electromyography (sEMG) signals as biofeedback training exercises might offer a promising path to improving swallowing function. The Rephagia biofeedback system uses sEMG to assess muscle strength, stamina, and timing of the swallowing action. objectives: The aim of this study was to evaluate the feasibility of the Rephagia system in ICU patients with dysphagia. Methods: This feasibility study included patients admitted to a 14-bed mixed medical-surgical ICU. All patients underwent a new tracheostomy placement during ICU stay due to persistent aspiration and ICU-acquired weakness, accompanied by verified dysphagia. Following Rephagia training, patients completed a questionnaire assessing comprehension, satisfaction, and motivation. Swallowing characteristics were assessed via mean sEMG peak values during exercise. Results: Twenty patients with a mean age of 69.4 (SD 8.2) years were included. The means of sEMG values at the beginning of a measurement were not significantly different at baseline versus everyone's last measurement (52 µV [23 µV] vs 57 µV [22 µV]; P=.50). The means of sEMG values obtained at the end of a measurement were not significantly different at baseline versus everyone's last measurement (56 µV [18 µV] vs 59 µV [23 µV]; P=.62). However, dysphagia improved in all patients. Patients understood the importance of the game in relation to their swallowing problems (16/80, 89%), which kept them motivated to participate in the training sessions (9/18, 50%). Conclusions: The Rephagia biofeedback system for stimulating swallowing actions in tracheotomized ICU patients with dysphagia is feasible. No relation was found between clinical improvement in swallowing function and sEMG signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".