Comparison of Nasalance Scores Obtained with the Nasometers 6200 and 6450
Bibliographic record
Abstract
OBJECTIVE: The study had the goal of comparing the new Nasometer 6450 to the older model 6200 using synthetic test sounds and control participants. A particular focus of the investigation was on the test-retest variability of the instruments. MATERIALS AND METHODS: The Nasometers 6200 and 6450 were compared using square wave test sounds. Six repeated measurements of oral, balanced, and nasal test stimuli were recorded from 25 female participants over an average of 35 days. RESULTS: The synthetic test sounds demonstrated that the two nasometers obtained similar results for a range of frequencies. The results for the clinically normal participants revealed that nasalance scores from the two instruments were within 1-2 points, depending on the test sentence. Variability in scores increased with the proportion of nasal consonants in the sentence. Test-retest variability was between 6 and 8 points for more than 90% of the participants. Participants with higher nasalance scores for oral stimuli had higher between-session variability. CONCLUSIONS: The Nasometers 6200 and 6450 should yield comparable results in clinical practice. Depending on the phonetic content of the test materials, clinicians should allow for a 6- to 8-point between-session variability when interpreting nasalance scores.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".