How Much Residual Hearing Is ‘Useful’ for Music Perception with Cochlear Implants?
Bibliographic record
Abstract
AIM: To compare performance on a song recognition task of bilaterally combined electric and acoustic hearing (bimodal stimulation) with electric or acoustic hearing alone. METHODS: Subjects were 14 adults with cochlear implants (CI) who continued to use a hearing aid (HA) in one/both ears. Subjects were asked to identify excerpts from 15 popular songs, which were familiar to them, presented in a random order via a single loudspeaker. Presentation conditions were fixed in order: bimodal, CI alone and then HA alone. Musical excerpts were presented in each condition with and then without lyrics. RESULTS: In a subgroup of subjects (n = 8) with better low-frequency residual hearing (thresholds <85 dB hearing level (HL)), mean scores for bimodal stimulation were significantly greater than for CI alone. In addition, mean 'no lyrics' scores for HA alone (59.7%) were significantly greater than for CI alone (38.8%). All of these subjects considered bimodal stimulation to be the most enjoyable way to listen to music. For the remaining subjects (n = 6) there was no benefit from using bimodal stimulation over CI alone, and the majority of these preferred to listen to music using CI alone. CONCLUSIONS: Bimodal stimulation provides better perception of popular music, particularly melody recognition, compared to CI alone when low-frequency residual hearing is better than 85 dB HL.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".