Differences in nasalance scores obtained with different Nasometer headsets
Bibliographic record
Abstract
The goal of the present research study was to investigate possible differences in nasalance scores between different Nasometer headgears. Frequency response characteristics of microphone pairs in a Nasometer model 6200, a model 6450 and two model 6500 headsets were compared using long-term average spectra of white noise and multi-speaker babble signals. Prerecorded sound files from a male and a female speaker were used to record nasalance scores with the four Nasometer headsets and to calculate cumulative absolute differences within and between the headsets. The main outcome measures were the cumulative absolute differences between the decibel (dB) values in the frequency bins from 300 to 750 Hz for the nasal and oral channels of each microphone pair. Cumulative absolute differences between nasalance scores of repeated stimuli within and across Nasometer headsets were tabulated. Results showed that cumulative absolute differences for the frequency range 300-750 Hz were between 6.58 and 7.68 dB. Within headsets, 95.6% to 100% of measurements of all four Nasometer headsets were within 3 nasalance points, although test-retest differences of up to 6 nasalance points were found. Between headsets, 56.1% to 98.9% of measurements were within 3 nasalance points, with the single largest difference of 8 nasalance points. In conclusion, differences between repeated nasalance scores obtained with the same and different headsets were noted. Clinicians should allow a margin of error of ±6 to 8 nasalance points when interpreting scores from different Nasometer headsets.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".