Predicting children’s real-ear-to-coupler differences based on tympanometric data
Bibliographic record
Abstract
OBJECTIVE: Paediatric hearing-aid verification relies on measures of output obtained from the ear canal or in a coupler with the child's real-ear-to-coupler difference (RECD). Measured RECD cannot always be completed in children, leading to fitting inaccuracies. Audiologists often have tympanometry data that characterises the child's ear-canal acoustics. The goal of this study was to determine if tympanometry can be used to improve predictions of measured RECD. DESIGN: A retrospective analysis of RECD and admittance, tympanometric peak pressure, and equivalent ear-canal volume from 226 Hz tympanometry collected as part of a longitudinal study of children with hearing loss were modelled with Bayesian hierarchical regression. STUDY SAMPLE: Two-hundred sixty-six children with mild-to-severe hearing loss contributed data. RESULTS: Age-based average RECD models were within 3 dB of measured RECD values in 54% of cases with normal middle ear status and 50.6% of cases with abnormal middle ear status. Immittance-predicted RECD were within 3 dB in 69.6% of cases with normal middle ear status and 74.4% of cases with abnormal middle ear status. CONCLUSION: Immittance-predicted RECD was more accurate than age-based average RECD, particularly in children with abnormal middle ear status. The findings suggest that 226 Hz tympanometry could be used clinically to improve predictions of measured RECD when it cannot be measured.
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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 |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 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".