Relationship of Head Circumference and Age in the Prediction of the Real-Ear-to-Coupler Difference (RECD)
Bibliographic record
Abstract
Background: Pediatric hearing instrument fitting is optimally performed with individually obtained realear-to-coupler difference (RECD) measurements. If these measurements cannot be obtained, predictedvalues based on age are used. Recent evidence obtained from children aged 3–11 years suggests thathead circumference (HC) may be a viable alternative or addition to age for use in RECD prediction.Purpose: The purpose of the present study was to determine if HC can be used to predict RECDs ininfants, children, and adults.Research Design: A correlational design was used. HC and RECD values were measured in allparticipants.Study Sample: Participants were 278 North American infants and children (136 males and 142 females)aged 1.6 months to 11 years and 109 adults (42 males and 67 females) aged 18 years to 83 years.Data Collection and Analysis: After otoscopic inspection and immittance measurements were performedto assess candidacy for inclusion in the study, HC was measured twice for all participantsand a single RECD measure was obtained for each participant at twelve frequencies (250 through12500 Hz). The reliability of HC measurements was assessed with an intraclass correlation analysis.Linear regression analyses were performed with age and HC as predictor variables and RECDs asthe dependent variable.Results: Analysis indicated good reliability of the HC measurement. The relationships between RECDand HC were comparable with the relationships between RECD and age. Combining HC and age did notimprove predictive accuracy.Conclusions: HC can be used in children and adults as an alternative metric in the prediction of RECDswhen individual RECDs cannot be obtained.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".