The Impact of Age on Noise Sensitivity in Cochlear Implant Recipients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of different open set sentence recognition tests in quiet, +10 dB signal to noise ratio (SNR), and +5 dB SNR in adult cochlear implant (CI) recipients above and below 65 years of age. STUDY DESIGN AND SETTING: Multi-institution, prospective, non-randomized, single-subject repeated measures design. PATIENTS: Ninety six adults more than or equal to 18 years old with postlingual bilateral sensorineural hearing loss. INTERVENTIONS: Participants received a CI532 in one ear. Speech perception measures were evaluated before and 6-months after activation. MAIN OUTCOME MEASURES: Subjects completed consonant-nucleus-constant (CNC) words in quiet and AzBio sentences in noise using +10 and +5 dB SNR, and Montreal Cognitive Assessment (MOCA). RESULTS: Ninety six adult patients were enrolled (n = 70 older [≥65 yr], n = 26 younger [<65 yr]). There was no difference in CNC scores (CI alone 58.4% versus 67.5%, p = 0.0857; best aided 66.7% versus 76.1%, p = 0.3357). Older adults performed worse on AzBio +10 dB SNR compared with younger patients (CI alone 37.4% versus 56.9%, p = 0.0006; best aided 51.4% versus 68.2%; p = 0.01), and in AzBio +5 dB SNR (CI alone 7.7% versus 11.2%, p = 0.0002; best aided 15.3% versus 22.3%, p = 0.0005). The magnitude of change in AzBio +10 dB SNR was significantly less in older adults in CI alone (15.3% versus 22.3%; p = 0.0493) but not best aided (21.5% versus 31.3%; p = 0.105). The magnitude of change was drastically worse in AzBio +5 dB SNR for older adults (CI alone 6.7% versus 22.1%, p = 0.0014; best aided 9.5% versus 21.5%; p = 0.0142). There was no significant difference in MOCA between the two age groups. CONCLUSIONS: While both older and younger patients have similar outcomes with respect to CNC word scores in quiet, the addition of noise disproportionally impacts older patients. Caution should be exercised testing the elderly in noise; testing in noise may disproportionally impact performance expectations and should be more carefully considered when used for candidacy criteria and counseling. Future studies need to further investigate the disproportionate effect of noise on candidacy testing and its impact on how elderly patients are qualified.
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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 | 0.003 |
| 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) | 0.001 | 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".