Can Differences in Early Hearing Development Be Distinguished by the LittlEARs Auditory Questionnaire?
Bibliographic record
Abstract
OBJECTIVE: This study asks whether the LittlEARs Auditory Questionnaire (LEAQ), a caregiver measure, can differentiate between the early auditory development of children with bilateral cochlear implants (CIs), bilateral hearing aids (HAs), and children with Auditory Neuropathy Spectrum Disorder (ANSD) who wear CIs or HAs. The LEAQ is sensitive to impaired auditory development but has not previously been used to distinguish developmental changes between groups of children using different hearing technologies or with different types of hearing loss. DESIGN: We collected retrospective longitudinal LEAQ results from 43 children with HAs, 43 with CIs, and 18 with ANSD. The children with ANSD wore hearing technology. They were a similar age to the children without ANSD (23 months; SD = 15), while the CI group (14 months; SD = 8) was younger than the HA group (24 months; SD = 18) [F(2,98.48) = 3.4; p = 0.04]. The CI group often participated in their first LEAQ pretreatment. Participants completed between one and seven LEAQs. Scores ranged between zero and 35 (mean = 18.36). We conducted a linear mixed-effects analysis, which included age or time since device fitting, hearing type (HA, CI, or ANSD), and presence of a comorbidity as fixed effects. A secondary analysis assessed effects of device audibility, measured by the Speech Intelligibility Index or Articulation Index, and consistency of device use obtained from device datalogs. RESULTS: Children with CIs progressed faster than their peers with HAs or ANSD [χ2(8) = 24.51; p = 0.002]. However, within a subsample that included consistency of device use (β7 = -0.20 ± 0.38, t = -0.52; β8 = 0.93 ± 0.82, t = 1.13) and audibility (β6 = -0.70 ± 1.45, t = -1.87; β7 = 0.87 ± 0.89, t = 0.98), study group did not significantly influence rate of improvement on the LEAQ. In addition, children with developmental delays in all three study groups demonstrated significantly slower LEAQ score improvement [χ2(6) = 23.60; p < 0.001] and a trend toward decreased consistency of device use [F(1) = 3.31; p = 0.07]. As we expected, children in the CI and HA groups were more likely to achieve auditory skills indicated in early rather than later LEAQ questions. There was less variability in the responses of the ANSD group [CI: interquartile range (IQR) = 9; HA: IQR = 8; ANSD: IQR = 1]. There was no connection between LEAQ growth and speech perception outcomes in a subsample [r(6) = 0.42; p = 0.30]. CONCLUSIONS: The LEAQ is a useful tool for monitoring initial auditory development in very young children and can inform early treatment decisions.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".