The Use of Biofeedback in the Treatment of Chronic Dysphagia in Stroke Patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of the use of surface electromyographic feedback in the treatment of stroke patients with chronic dysphagia. PATIENTS AND METHODS: Data of 11 consecutive patients with chronic dysphagia after stroke were analyzed. Our patients were treated for dysphagia with surface electromyography as biofeedback as adjunct to normal exercises. All patients suffered from dysphagia after stroke. The average time after onset was 31.1 months. All patients had been previously treated by speech therapists without success. Functional swallowing was estimated using the Functional Oral Intake Scale (FOIS). At the start of the treatment 8 patients were tube dependent (FOIS < or = 4). Three patients were on an oral diet, but with restrictions (FOIS > or = 5). RESULTS: The patients were treated on average seven 7 times. The time between the first and last treatment session was on average 76.1 days (SD +/- 44.0; range = 29-168). Before treatment the average FOIS was 2.6 (SD +/-2.3) and after treatment 5.6 (SD +/-1.6). The median scores improved from 1 to 6, showing a significant and clinically relevant improvement (z = -2.820: p < 0.01) in swallowing function. In 6 of initially 8 patients with percutaneous enteral gastrostomy tubes, the feeding tube could be removed after treatment. CONCLUSION: Our data suggest that the use of surface electromyography as biofeedback in the treatment of chronic dysphagia after stroke could be an effective adjunct to standard therapy for swallowing disorders in 11 patients.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".