Pitch Discrimination Testing in Patients with a Voice Disorder
Bibliographic record
Abstract
Auditory perception plays an important role in voice control. Pitch discrimination (PD) is a key index of auditory perception and is influenced by a variety of factors. Little is known about the potential effects of voice disorders on PD and whether PD testing can differentiate people with and without a voice disorder. We thus evaluated PD in a voice-disordered group (n = 71) and a non-voice-disordered control group (n = 80). The voice disorders included muscle tension dysphonia and neurological voice disorders and all participants underwent PD testing as part of a comprehensive voice assessment. Percentage of accurate responses and PD threshold were compared across groups. The PD percentage accuracy was significantly lower in the voice-disordered group than the control group, irrespective of musical background. Participants with voice disorders also required a larger PD threshold to correctly discriminate pitch differences. The mean PD threshold significantly discriminated the voice-disordered groups from the control group. These results have implications for the voice control and pathogenesis of voice disorders. They support the inclusion of PD testing during comprehensive voice assessment and throughout the treatment process for patients with voice disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".