Improvements in Perceived Abilities and Speech Recognition in Quiet and Noise for Older Adults With Single-sided Deafness or Asymmetric Hearing Loss: A Multi-center Investigation
Bibliographic record
Abstract
OBJECTIVE: Cochlear implantation is an effective treatment option for individuals with single-sided deafness (SSD) and asymmetric hearing loss (AHL). Most data are from young and middle-aged adults. This study assessed the outcomes of cochlear implant (CI) use for older adults. STUDY DESIGN: Multi-center, prospective, repeated-measures. SETTING: Five academic centers. PATIENTS: A total of 39 older adult (60 yr or older) CI users with SSD or AHL. INTERVENTION: Cochlear implantation. MAIN OUTCOME MEASURES: Procedures were completed preoperatively with a rerouting device and 3, 6, and 12 months postactivation with the CI. Speech recognition for the affected ear was assessed with CNC words. Speech recognition in noise was assessed with the AzBio sentences. The target was presented from the front loudspeaker and the masker was co-located with the target, 90 degrees toward the better hearing ear, or 90 degrees toward the affected ear. Perceived abilities were assessed with the Speech, Spatial, and Qualities of hearing scale. RESULTS: Participants demonstrated significant improvements over time on all measures ( p <0.001). There was no significant effect of age on CNC scores ( p =0.493) or perceived abilities ( p =0.314). Age ( p =0.031) and contralateral hearing level ( p <0.001) significantly influenced speech recognition in noise. The majority of participants experienced similar or improved performance in noise compared with their own preoperative abilities. CONCLUSIONS: Older adults with SSD and AHL demonstrate significant benefit from cochlear implantation, supporting its effectiveness for Centers for Medicare and Medicaid Services beneficiaries. Candidates should be counseled on how age and hearing level in the better hearing ear may affect outcomes, particularly in noisy environments.
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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 | 0.001 | 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) | 0.001 | 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".