Factors Associated with Speech-Recognition Performance in School-Aged Children with Cochlear Implants and Early Auditory-Verbal Intervention
Bibliographic record
Abstract
Abstract Background Considerable variability exists in the speech recognition abilities achieved by children with cochlear implants (CIs) due to varying demographic and performance variables including language abilities. Purpose This article examines the factors associated with speech recognition performance of school-aged children with CIs who were grouped by language ability. Research Design This is a single-center cross-sectional study with repeated measures for subjects across two language groups. Study Sample Participants included two groups of school-aged children, ages 7 to 17 years, who received unilateral or bilateral CIs by 4 years of age. The High Language group (N = 26) had age-appropriate spoken-language abilities, and the Low Language group (N = 24) had delays in their spoken-language abilities. Data Collection and Analysis Group comparisons were conducted to examine the impact of demographic characteristics on word recognition in quiet and sentence recognition in quiet and noise. Results Speech recognition in quiet and noise was significantly poorer in the Low Language compared with the High Language group. Greater hours of implant use and better adherence to auditory-verbal (AV) therapy appointments were associated with higher speech recognition in quiet and noise. Conclusion To ensure maximal speech recognition in children with low-language outcomes, professionals should develop strategies to ensure that families support full-time CI use and have the means to consistently attend AV appointments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".