Assessment of Nasal-Noise Masking Audiometry as a Diagnostic Test for Patulous Eustachian Tube
Bibliographic record
Abstract
OBJECTIVE: The primary objective is to assess the validity of nasal-noise masking audiometry (NNMA) as a clinical diagnostic tool in our patient population. STUDY DESIGN: Retrospective case review. SETTING: Tertiary ambulatory referral center. PATIENTS: Patients with patulous Eustachian tube (PET) were identified from referrals to our Eustachian tube disorders clinic primarily with symptoms including autophony, aural fullness, and hearing their own breathing. The healthy subjects had no history of ear disease. INTERVENTION: NNMA was measured in 20 ears of 10 healthy subjects as well as in 42 ears of 21 patients with suspected PET. MAIN OUTCOME MEASURE: NNMA mean auditory thresholds were measured at frequencies ranging from 250 to 8,000 Hz. RESULTS: When stratified as definitive or probable PET based on observed tympanic membrane movement with breathing, both Definitive and Probable PET groups had significantly higher NNMA mean auditory thresholds compared to Normal ears at 250 Hz (p = 0.001, p = 0.003), 1,000 Hz (p = 0.019, p = 0.001), and 6,000 Hz (p = 0.4, p = 0.001). When stratified based on symptoms on the day of testing, both Symptomatic Ears and Non-Symptomatic Ears had significantly higher mean auditory thresholds compared to Normal ears at 250 Hz (p = 0.001, p = 0.015) and at 1,000 Hz (p = 0.002, p = 0.004). CONCLUSION: Our results demonstrate a larger masking effect in patients with PET compared to normal subjects in the low-frequency region. In clinical practice, the relatively small effect and the wide variability of results between patients have made this test be of little value clinically in our patient population.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".