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 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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".