Long-term outcome after cochlear implantation in children with additional developmental disabilities
Bibliographic record
Abstract
OBJECTIVE: Candidacy criteria for cochlear implants have expanded to include children with complex developmental disabilities. The aim of this study was to determine the long-term benefits of cochlear implantation for this clinical population. DESIGN: The study involved a retrospective chart review. STUDY SAMPLE: The review identified 21 children with complex disabilities who had received cochlear implants in a pediatric center prior to 2004. Length of cochlear implant use was between 7.3 and 19.0 years. Long-term functional auditory abilities were assessed pre and post-operatively using measures appropriate to the child's level of functioning. Cognitive assessments and developmental data were also available for the children. RESULTS: Children's long-term speech recognition outcomes depended highly on their developmental status. Children with severe developmental delay showed no open-set speech recognition abilities while children with mild to moderate delays achieved open-set scores ranging from 48 to 94% on open-set word testing. Five of 13 (38%) children with complex needs had discontinued use of their cochlear implant. CONCLUSIONS: Long-term speech recognition abilities following cochlear implantation for children with complex developmental issues seem to be highly related to their developmental profile. Developmental status is an important consideration in counselling families as part of the cochlear implant decision process.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".