Computer-Assisted Audiometry Versus Manual Audiometry
Bibliographic record
Abstract
OBJECTIVE: The Otogram is an automated computer-assisted audiometer that allows patients to self-administer audiometry for their pure-tone audiogram. There has been no research published in a peer-reviewed journal, validating its use in an otology outpatient clinic. We therefore proposed to investigate and compare the inter-rater and intrarater accuracy and reliability of audiologists and of the Otogram in an English-speaking British population. DESIGN: Prospective nonrandomized validation study. SETTING: Secondary otolaryngology center and otology outpatient clinic. PARTICIPANTS: Forty-eight NHS patients referred to an otology outpatient clinic. MAIN OUTCOME MEASURES: Each patient had 2 pure-tone audiograms. Hearing thresholds in decibels hearing level were ascertained by fully trained British audiologists and by the Otogram. RESULTS: Using the weighted kappa statistic, the level of agreement in air-conduction (kappa = 0.965) and bone-conduction (kappa = 0.927) thresholds between the audiologist and the Otogram on the same patient was equivalent to the inter-rater level of agreement between pairs of audiologists. Approximately 94% of air-conduction thresholds and 91% of bone-conduction thresholds measured by the Otogram fell within 10 dB of thresholds measured by an audiologist. Intrarater comparisons between audiologists were very good for air-conduction (kappa = 0.978) and bone-conduction (kappa = 0.964). The intrarater level of agreement between repeated Otogram thresholds was just as good for air-conduction (kappa = 0.974) and bone-conduction (kappa = 0.945) thresholds. CONCLUSION: The Otogram is just as reliable as audiologists at determining hearing thresholds. We recommend that the Otogram can be safely used in a controlled clinical setting supervised by audiologists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".