Users’ experience of a cochlear implant combined with a hearing aid
Bibliographic record
Abstract
This study examined: (1) the prevalence of hearing-aid use in a clinical population of adults with unilateral cochlear implants, (2) the relationship between hearing-aid use, severity of hearing loss, duration of deafness and duration of cochlear implant use, and (3) the benefits of bimodal hearing from the users' perspective. Using a retrospective design, 31 adults were identified as bimodal users, and 93 adults implanted in the same period were identified as non hearing-aid users. The two groups were similar in regards to duration of deafness but differed in severity of hearing loss and time since implantation. Questionnaires examining frequency and situations of hearing-aid use were completed by 24 of 31 bimodal users. Fifteen of these 24 adults reported hearing-aid use more than 50% of the time. These findings suggest that, of the 72 adults in this study with useable hearing (pure-tone average better than 110 dB), about 30% or less regularly combined a hearing aid and cochlear implant. The questionnaire results suggest that regular bimodal users prefer bimodal hearing across a variety of listening environments such as music, noise, and reverberation.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".