A Prototype Low-Cost Pharyngeal Vibration Device for Voice Rehabilitation Following Laryngectomy
Bibliographic record
Abstract
Laryngeal cancer disproportionately affects socioeconomically disadvantaged patients and its incidence is increasing in low-resource environments. Surgical ablation with laryngectomy results in loss of voice necessitating rehabilitation, for which the current devices are prohibitively expensive or difficult to repair. In this study, we developed a prototype phonation device with accessible and affordable electronic and mechanical components. Material and vibration wave properties were chosen after modelling a modified longitudinal wave equation. The device consists of a 3D-printed cylinder with a tip-mounted oscillating solenoid controlled by a timer circuit to propagate longitudinal waves to the neck. Speech intelligibility and volume were assessed on healthy volunteers by measuring the accuracy of words heard by a listener after a brief introduction to device technique. This was repeated with a commercial electrolarynx device for comparison. The mean accuracy of the word recorded was 0.956 (IQR 0.940 - 1.000) with an audible frequency of 57Hz to 138Hz. The device demonstrated listener accuracy statistically similar to commercial devices with phonation frequencies that in range of average human voice. The device was more affordable than commercial devices (under 35 USD compared to 600 USD) with common electronic components obtainable from international retailers. The results provide motivate for further development with the goal of open-source distribution of a blueprint to be manufactured remotely in in low-resource settings.
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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 | 0.001 |
| 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 | 0.001 | 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".