Factors Affecting the Development of Speech, Language, and Literacy in Children With Early Cochlear Implantation
Bibliographic record
Abstract
PURPOSE: This study investigated factors contributing to auditory, speech, language, and reading outcomes in children with prelingual deafness after 4-6 years of multichannel cochlear implant use. The analysis controlled for the effects of child, family, and implant characteristics so that educational factors most conducive to maximum implant benefit could be identified. METHOD: The sample included 136 8- and 9-year-old children from across the United States and Canada who were implanted by age 5 with the Nucleus 22-channel implant. Type and amount of educational intervention since implantation constituted the independent variables. The dependent variable was performance on a battery of tests of speech perception, speech production, language, and reading administered 4-6 years postimplant. Characteristics of the child, the family, and the implant itself constituted intervening variables. A series of multiple regression analyses determined the amount of variance in each outcome accounted for by the intervening variables and the amount of additional variance attributable to independent variables. RESULTS: Characteristics of the child and the family (primarily nonverbal IQ) accounted for approximately 20% of the variance in postimplant outcome. An additional 24% was accounted for by implant characteristics and 12% by educational variables, particularly oral communication mode. CLINICAL IMPLICATIONS: Auditory, speech, language, and reading skills achieved 4-6 years after cochlear implantation were most strongly associated with nonverbal IQ, implant functioning, and use of an oral communication mode.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".