Development and evaluation of a new intraoral voice assist device called the voice retriever
Bibliographic record
Abstract
Objective: Patients lose their voice after laryngectomy for laryngeal cancer or aspiration prevention surgery for severe dysphagia. To assist such patients, we developed and verified the utility of a novel vocalization method using a device termed the voice retriever (VR), in which the sound source is placed in the mouth. Methods: We investigated the effectiveness of the VR in patients. The VR consists of a mouthpiece with a built-in speaker and a dedicated application that serves as the sound source. We compared the speech intelligibility and naturalness in normal participants using VR and an electrolarynx (EL) for the first time as well as the voice-related quality of life (V-RQOL) in patients with dysphonia before and after using the VR. Results: The VR produced significantly higher 100-syllable test scores as well as fluency, amount of additional noise, intonation, intelligibility and overall long reading test ratings in the first-time VR and EL users. Furthermore, the VR use significantly improved the V-RQOL of participants with dysphonia. Conclusion: Compared to EL, VR allows more effective speech improvement in participants without experience using an alternative vocalization method and improves the V-RQOL in patients with dysphonia. Level of Evidence: Step 4.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".