Music perception improves in children with bilateral cochlear implants or bimodal devices
Bibliographic record
Abstract
The objectives of this study were to determine if music perception by pediatric cochlear implant users can be improved by (1) providing access to bilateral hearing through two cochlear implants or a cochlear implant and a contralateral hearing aid (bimodal users) and (2) any history of music training. The Montreal Battery of Evaluation of Musical Ability test was presented via soundfield to 26 bilateral cochlear implant users, 8 bimodal users and 16 children with normal hearing. Response accuracy and reaction time were recorded via an iPad application. Bilateral cochlear implant and bimodal users perceived musical characteristics less accurately and more slowly than children with normal hearing. Children who had music training were faster and more accurate, regardless of their hearing status. Reaction time on specific subtests decreased with age, years of musical training and, for implant users, better residual hearing. Despite effects of these factors on reaction time, bimodal and bilateral cochlear implant users' responses were less accurate than those of their normal hearing peers. This means children using bilateral cochlear implants and bimodal devices continue to experience challenges perceiving music that are related to hearing impairment and/or device limitations during development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".