Clinical Application of a New Approach to Identify Oral–Nasal Balance Disorders Based on Nasalance Scores
Bibliographic record
Abstract
OBJECTIVE: A new approach to classify oral-nasal balance disorders based on instrumental measurements was developed based on linear discriminant analysis (LDA) of nasalance scores of simulated oral-nasal balance disorders by de Boer and Bressmann. The current study aimed to apply the newly developed functions to clinical data to investigate the applicability of this new approach. DESIGN: Retrospective diagnostic accuracy study. SETTING: Tertiary university hospital. PARTICIPANTS: Fifty-five Dutch-speaking Flemish children (age 4-12 years) with normal (n = 20), hypernasal (n = 18), hyponasal (n = 12), or mixed nasality (n = 5). INTERVENTIONS: Nasalance scores of an oral and a nasal text were used to calculate 3 sets of LDA function scores. Predicted classification was consecutively based on the function values of the group centroids originally determined by de Boer and Bressmann and adapted LDA functions and group centroids based on clinical data. MAIN OUTCOME MEASURES: Discriminatory power of the linear discriminant formulas. RESULTS: Based on the original LDA functions, 56% of the speech samples matched the perceptual classification. Applying a correction factor for age and language differences resulted in a 67% correct classification, although 83% of the hyponasal samples were ranked as "normal resonance." Rederivation of the LDA functions based on current clinical data resulted in an 80% correct classification. CONCLUSIONS: The new approach of classifying oral-nasal balance disorders based on a combination of nasalance scores was promising. However, further clinical research is needed to refine the LDA functions and group centroids before clinical application is possible.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".