Effects of Age at Cochlear Implantation on Vocabulary and Grammar: A Review of the Evidence
Bibliographic record
Abstract
Purpose The increasing prevalence of pediatric cochlear implantation over the past 25 years has left little doubt that resulting improvements in hearing offer significant benefits to language development for many deaf children. Furthermore, given the documented importance of access to language from birth, there has been strong support for providing congenitally deaf children with implants as early as possible. Earliest implantation, in many ways, has become the "gold standard" in pediatric cochlear implantation, on the assumption that it is the key to language development similar to that of hearing children. Empirical evidence to support this assumption, however, appears more equivocal than generally is believed. This article reviews recent research aimed at assessing the impact of age at implantation on vocabulary and grammatical development among young implant users. Method Articles published between 2003 and 2018 that included age at implantation as a variable of interest and in which it was subjected to statistical analysis were considered. Effect sizes were calculated whenever possible; we conducted a multivariate meta-analysis to compare outcomes in different language domains. Results Taken together, findings from 49 studies suggest that age at implantation is just one of a host of variables that influence vocabulary and grammatical development, its impact varying with several factors including whether age at implantation is treated as a dichotomous or continuous variable. Results from a meta-analysis showed significant differences across language domains. Conclusion The pattern of results obtained indicates the importance of considering various child, family, and environmental characteristics in future research aimed at determining how early "early implantation" needs to be and the extent to which age at implantation, duration of implant use, and other factors influence language and language-related outcomes. Supplemental Material https://doi.org/10.23641/asha.9789041.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".