The Benefit of a Wireless Contralateral Routing of Signals (CROS) Microphone in Unilateral Cochlear Implant Recipients
Bibliographic record
Abstract
OBJECTIVE: Assess speech outcomes in unilateral cochlear implant (CI) recipients after addition of a wireless contralateral routing of signals (CROS) microphone. STUDY DESIGN: Prospective cohort study. SETTING: Ambulatory. PATIENTS: Sixteen adult unilateral CI users with nonserviceable hearing on the contralateral side were recruited. Those with AzBio sentence scores of 40 to 80% or Hearing in Noise Test - Quiet (HINT-Q) scores of 60 to 90% with a CI alone were eligible participants. INTERVENTION: Speech testing was carried out with the CROS on and off. MAIN OUTCOME MEASURE: Speech recognition. RESULTS: In the consonant-nucleus-consonant test presented in quiet from the front, word scores were 64.4 (CI) and 63.8% (CI + CROS) (p = 0.72), and phoneme scores were 80.2 (CI) and 80.8% (CI + CROS) (p = 0.65). In AzBio sentence testing in quiet, with the signals projected from the contralateral, front, or ipsilateral to the CI, speech perception with the CI alone was 60.8, 75.9, and 79.1%. With the addition of the CROS microphone, using the same speaker arrangement, speech perception was 69.8 (p < 0.05), 71.8 (p = 0.05), and 71.8 (p < 0.05). In AzBio sentence testing in noise, speech perception with the CI alone was 18.6, 45.3, and 56.3% when signals were projected from contralateral, front, and ipsilateral sides to the CI. The addition of the CROS microphone led to speech perception of 45.3 (p < 0.05), 45.3 (p = 0.86), and 51.4% (p = 0.27) in the same paradigm. CONCLUSIONS: Addition of a wireless CROS microphone to a unilateral CI recipient can improve users' perception of speech in both quiet and noise if speech signals come from the deaf ear, mitigating the head shadow effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".