Benefits and risks related to cochlear implantation for children with residual hearing: a systematic review
Bibliographic record
Abstract
OBJECTIVE: This study aimed to synthesise information concerning the potential benefits and risks related to cochlear implants (CIs) versus hearing aids (HAs) in children with residual hearing. DESIGN: A systematic review of articles published from January 2003 to January 2019 was conducted. STUDY SAMPLE: Our review included studies that compared the benefits and risks of CIs versus HAs in children (≤18 years old) with residual hearing. A total of 3265 citations were identified; 8 studies met inclusion criteria. RESULTS: Children with CIs showed significantly better speech perception scores post-CI than pre-CI. There was limited evidence related to improvement in everyday auditory performance, and the results showed non-significant improvement in speech intelligibility. One study on social-emotional functioning suggested benefits from CIs. In four studies, 37.2% (16/43) of children showed loss of residual hearing and 14.0% (8/57) had discontinued or limited use of their device. CONCLUSIONS: Children with CIs showed improvement in speech perception outcomes compared to those with HAs. However, due to the limited number of studies and information to guide decision-making related to other areas of development, it will be important to conduct further research of both benefits and risks of CIs in this specific population to facilitate decision-making.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".