Cognitive improvement after cochlear implantation in deaf children with associated disabilities
Bibliographic record
Abstract
AIM: To monitor functional auditory and non-verbal cognitive skills in children with cochlear implants who had associated disabilities over a 24-month period and define how cochlear implantation may impact on non-verbal cognition by restoring functional auditory skills. METHOD: Sixty-four children with cochlear implants (36 females, 28 males; mean age 4y 3mo, SD 3y 5mo, 9mo-14y 5mo) were recruited and divided into three groups: children with typical development group (TDG); children with associated disabilities not linked to non-verbal cognitive disorders group (ADG1); and children with associated disabilities linked to non-verbal cognitive disorders group (ADG2). Tests of functional auditory, communicative, and non-verbal cognitive skills were performed before cochlear implantation and at 12 and 24 months after cochlear implantation. RESULTS: Functional auditory and communicative skills improved similarly in the three groups at 12 and 24 months after implantation. An increase in non-verbal cognitive scores was present in children in the ADG2 from baseline to 12 and 24 months (p<0.01), whereas scores remained stable in children in the TDG and ADG1. The increased functional auditory skills scores after cochlear implantation corresponded to an increase in non-verbal cognitive scores (p=0.032) in children in the ADG2. INTERPRETATION: Children with associated disabilities, especially if linked to non-verbal cognitive disorders, benefitted from cochlear implantation. They improved their comprehension of acoustic information inferred from the environment, improving not only functional auditory skills but also non-verbal cognition.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".