Reliability and Accuracy of a Method of Adjustment for Self-Measurement of Auditory Thresholds
Bibliographic record
Abstract
OBJECTIVE: To evaluate the reliability and accuracy of a method for measuring pure tone air conduction thresholds in which the user adjusts test tones to threshold, using an iPad, automated instructions, and minimal supervision. STUDY DESIGN: Prospective nonrandomized validation study. SETTING: University hearing research laboratories and audiology clinics. PATIENTS: Fifty-five adults with hearing loss in at least 1 ear ranging from mild to severe. INTERVENTION: Automated measurement of pure tone air conduction thresholds using the following: a software-controlled adaptive method, and a user-controlled method of adjustment, both implemented on a calibrated iPad and using standard audiometry earphones. MAIN OUTCOME MEASURE: Test-retest reliability of both methods, comparison of thresholds measured with automated techniques to thresholds measured using manual audiometry. RESULTS: For both iPad methods, test-retest differences were smaller than those reported in other studies for manual audiometry. Average automated versus manual threshold differences were within the range of expected variance of manual audiometry. Subjects preferred the adjustment method. CONCLUSION: Both iPad self-test methods yield accurate and reliable pure tone air conduction thresholds.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".