Predictors of cochlear implant outcomes in pediatric auditory neuropathy: A matched case-control study
Bibliographic record
Abstract
OBJECTIVES: Current evidence supports the benefits of cochlear implants (CIs) in children with hearing loss, including those with auditory neuropathy spectrum disorder (ANSD). However, there is limited evidence regarding factors that hold predictive value for intervention outcomes. DESIGN: This retrospective case-control study consisted of 66 children with CIs, including 22 with ANSD and 44 with sensorineural hearing loss (SNHL) matched on sex, age, age at CI activation, and the length of follow-up with CIs (1:2 ratio). The case and control groups were compared in the results of five open-set speech perception tests, and a Forward Linear Regression Model was used to identify factors that can predict the post-CI outcomes. RESULTS: There was no significant difference in average scores between the two groups across five outcome measures, ranging from 88.40% to 95.65%. The correlation matrix revealed that younger ages at hearing aid fitting and CI activation positively influenced improvements in speech perception test scores. Furthermore, among the variables incorporated in the regression model, the duration of follow-up with CIs, age at CI activation, and the utilization of two CIs demonstrated prognostic significance for improved post-CI speech perception outcomes. CONCLUSIONS: Children with ANSD can achieve similar open-set speech perception outcomes as children with SNHL. A longer CI follow-up, a lower age at CI activation, and the use of two CIs are predictive for optimal CI outcome.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".