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 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) | 0.002 | 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".