Automated hearing tests: applying the otogram to patients who are difficult to test.
Bibliographic record
Abstract
OBJECTIVE: The Otogram is an automated audiometer capable of determining air and bone conduction thresholds with masking when appropriate. The manufacturer claims that testing can be done in a quiet physician's consultation room without a sound-treated booth. We aimed to test the validity of the Otogram on "difficult-to-test" patients, all of whom require masking. METHODS: Twenty-eight difficult-to-test patients underwent three audiograms: two by an audiologist and one by the Otogram. First, audiograms performed by the audiologists were compared, establishing test-retest reliability. Second, audiograms performed by the Otogram were compared to those of the audiologists. We calculated the percentage of pure-tone thresholds that were in agreement by 10 dB. Weighted kappa statistical analyses demonstrated levels of agreement. RESULTS: Comparisons between audiologists demonstrated a very high degree of agreement. More than 90% of air and bone conduction thresholds fell within 10 dB of each other. Comparisons between audiologists and the Otogram also demonstrated a high degree of agreement. CONCLUSIONS: The Otogram has the capability to accurately ascertain air and bone conduction thresholds. It appropriately used masking when indicated. The Otogram has great potential as a diagnostic tool to improve access to health care, especially where hearing test facilities are limited or unavailable.
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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 | 0.001 | 0.012 |
| 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 | 0.001 |
| 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".