Nasalance-Based Preclassification of Oral–Nasal Balance Disorders Results in Higher Agreement of Expert Listeners’ Auditory-Perceptual Assessments: Results of a Retrospective Listening Study
Bibliographic record
Abstract
OBJECTIVE: Reliable perceptual and instrumental assessment of oral-nasal balance disorders is a persistent problem in speech-language pathology. The goal of the study was to evaluate whether nasalance-based preclassification of oral-nasal balance disorders improves listener agreement. DESIGN: Retrospective listening study. SETTING: Tertiary university hospital. PARTICIPANTS: Fifty-four randomly selected recordings of patients with repaired unilateral cleft lip and palate (UCLP). Three experienced speech-language pathologists participated as expert listeners. INTERVENTIONS: Two listening experiments were based on nasalance scores and audio recordings of speakers with repaired UCLP. The speakers were preclassified as normal, hypernasal, hyponasal, or mixed based on their nasalance scores. Initially, the listeners determined the diagnostic category of the oral-nasal balance for 62 audio recordings (8 repeats). Six months later, they listened to 38 of the recordings (6 repeats) along with a spreadsheet indicating the nasalance-based categories for the oral-nasal balance. The listeners confirmed, or rejected and corrected, the nasalance-based preclassification. MAIN OUTCOME MEASURES: Intralistener, interlistener agreement, and agreement between listener categories and nasalance-based oral-nasal balance categories. RESULTS: In the first study, the agreement between the listeners' diagnostic category and the nasalance-based category was 45.1% and the interlistener agreement was 36.7%. In the second study, the agreement between the listeners' category and the nasalance-based category was 67.1% (75% agreement for the correct nasalance-based categories and 41.7% for the misclassifications), and the interlistener agreement was 85.4%. CONCLUSIONS: Preclassification of oral-nasal balance disorders based on nasalance scores may help listeners achieve better diagnostic accuracy and higher agreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".