Predicting wideband real-ear-to-coupler differences in children using wideband acoustic immittance
Bibliographic record
Abstract
Individual differences in ear-canal acoustics introduce variability into hearing aid output that can affect speech audibility. Measuring ear-canal acoustics in young children can be challenging, and relying on normative real-ear-to-coupler difference (RECD) transforms can lead to large fitting errors. Acoustic immittance measures characterize the impedance of the ear and are more easily measured than RECD. Using 226 Hz tympanometry to predict the RECD is more accurate than using age-based average RECD values. The current study sought to determine whether wideband acoustic immittance measurements could improve predictions of wideband real-ear-to-coupler difference (wRECD). 150 children ages 2-10 years with intact tympanic membranes underwent wRECD and wideband acoustic immittance measures in each ear. Three models were constructed to predict each child's measured wRECD: the age-based average wRECD, 226 Hz admittance wRECD, and wideband absorbance wRECD. The average age-based wRECD model predicted the child's measured wRECD within 3 dB in 62% of cases, but both the 226 Hz admittance and wideband absorbance wRECD were within 3 dB in 90% of cases. Using individual 226 Hz or wideband absorbance to predict wRECD improved the accuracy and precision of transforms used for pediatric hearing aid fitting.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".