Interpretation of sonotubometric data based on phase-shift detection
Bibliographic record
Abstract
BACKGROUND: Sonotubometry is a non-invasive means of assessing Eustachian tube (ET) function. Its interpretation remains a complex task with questionable results due to wide variation between trials. A study was conducted to ascertain whether the measurement of phase shift in sonotubometric signals would be a more reliable indicator of ET patency than fluctuating Sound Pressure Level (SPL). METHODS: The ears of six healthy participants and two participants with patulous ET (PET) were probed with a 100 Hz signal. Five recordings of SPL were performed at the external auditory canal. Cross-correlation was performed among filtered SPL signals and among extracted phase shift waveforms. Peak coefficients were averaged to provide a measure of waveform similarity between trials. RESULTS: Mean peak cross correlation coefficient for SPL signal measured 0.603 ± 0.057 Standard Error of Mean (SEM) whilst that for Phase-Shift signal measured 0.884 ± 0.027 (SEM). All normal participants demonstrated an observable phase change between the ear and nasal signal during swallowing indicating an acoustic impedance change during the event. For the PET patients tested, the phase measurements in ear and nasal signals follow one another reasonably closely, indicating little or no impedance change during swallowing. It is thought that this impedance change is indicative of opening of the ET in normal patients, and the lack impedance change indicates ET either remaining open or remaining closed throughout the swallow. CONCLUSIONS: Experimental data suggest that phase-shift detection is a more consistent means of interpreting sonotubometric data than SPL analysis.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".