Mobile tablet audiometry in fluctuating autoimmune ear disease
Bibliographic record
Abstract
BACKGROUND: Autoimmune inner ear disease (AIED) is a rare condition characterized by bilateral fluctuating sensorineural hearing loss (SNHL). The labile nature of this hearing loss makes it difficult to accurately quantify with conventional methods, and therefore it is challenging to rehabilitate. METHODS: Over a 9-month period one pediatric patient with severe AIED was monitored and conducted home audiograms using a previously validated testing system (Shoebox Audiometry). During this period he also underwent several clinical audiograms. The correlation between clinical and home audiograms was analyzed with a Pearson coefficient, and the range and frequency of fluctuations was recorded. RESULTS: Sixty-four automated home audiograms and nine clinical audiograms were conducted. When tested at home using a calibrated system the pure tone average (PTA) fluctuated between 12 dB and 72 dB indicating large variability in hearing. Fluctuations were frequent: on 28 occasions the PTA varied by at least 5 dB when retested within 4 days. The mean PTA was 50 dB and 95% of the thresholds were within 36 dB of the mean. Clinical audiograms obtained on the same day or within 1 day of home testing were highly concordant (with a Pearson coefficient of 0.93). CONCLUSION: AIED can result in significant fluctuations in hearing over short periods of time. Home testing enables a more granular look at variations over time and correlates well with clinical testing, and thus facilitates rapid action and informed rehabilitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".