Is there a critical period for cochlear implantation in congenitally deaf children? Analyses of hearing and speech perception performance after implantation
Bibliographic record
Abstract
A range of basic and applied studies have demonstrated that during the development of the auditory system, early experimental manipulations or clinical interventions are generally more effective than those made later. We present a short review of these studies. We investigated this age-related plasticity in relation to the timing of cochlear implantation in deaf-from-birth children. Cochlear implantation is a standard intervention for providing hearing in children with severe to profound deafness. An important practical question is whether there is a critical period or cutoff age of implantation after which hearing outcomes are significantly reduced. In this article, we present data from prelingually deaf children (mostly congenitally deaf) implanted at ages ranging from 1 to 15 years. Each child was tested with auditory and speech understanding tests before implantation, and at regular intervals up to 8 years postimplantation. We measured the improvement in performance of speech understanding tests in younger implanted children and compared it with the results of those implanted at a later age. We also used a binary partitioning algorithm to divide the data systematically at all ages at implant to determine the optimum split, i.e., to determine the age at implant which best separates performance of early implanted versus later implanted children. We observed distinct age-of-implant cutoffs, and will discuss whether these really represent critical periods during development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".