Telemedicine‐enabled biofeedback electropalatography rehabilitation (TEBER): A pilot study for patients treated with surgery for oral cavity carcinoma
Bibliographic record
Abstract
BACKGROUND: To address the rehabilitative barriers to frequency and precision of care, we conducted a pilot study of a biofeedback electropalatography (EPG) device paired with telemedicine for patients who underwent primary surgery +/- adjuvant radiation for oral cavity carcinoma. We hypothesized that lingual optimization followed by telemedicine-enabled biofeedback electropalatography rehabilitation (TEBER) would further improve speech and swallowing outcomes after "standard-of-care" SOC rehabilitation. METHOD: Pilot prospective 8-week (TEBER) program following 8 weeks of (SOC) rehabilitation. RESULTS: Twenty-seven patients were included and 11 completed the protocol. When examining the benefit of TEBER independent of standard of care, "range-of-liquids" improved by +0.36 [95% CI, 0.02-0.70, p = 0.05] and "range-of-solids" improved by +0.73 [95% CI, 0.12-1.34, p = 0.03]. There was a positive trend toward better oral cavity obliteration; residual volume decreased by -1.2 [95% CI, -2.45 to 0.053, p = 0.06], and "nutritional-mode" increased by +0.55 [95% CI, -0.15 to 1.24, p = 0.08]. CONCLUSION: This pilot suggests that TEBER bolsters oral rehabilitation after 8 weeks of SOC lingual range of motion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".