Talk to my better ear: Consequences of asymmetric bilateral hearing in development
Bibliographic record
Abstract
We study consequences of asymmetric hearing in children with bilateral profound deafness who used a unilateral cochlear implant before bilateral implantation and children who had access to acoustic hearing in one ear with or without a hearing aid before receiving a cochlear implant in the opposite ear (bimodal). Electrophysiological and behavioral measures post-implantation reveal benefits of bilateral implant/bimodal device use but also persistent consequences of early asymmetric auditory input. Auditory cortices maintain an aural preference for the first hearing ear despite chronic bilateral/bimodal hearing. Asymmetries are also evident in speech detection and perception; children receiving bilateral cochlear implants with long delays continue to favour their first implanted ear in both quiet and noise. Children with bimodal devices who have insufficient hearing in the non-implanted ear begin to prefer to listen with the cochlear implant. Impaired binaural hearing in groups with asymmetric function are evidenced by poor integration of input from the two devices and impaired perception and cortical processing of interaural timing differences. Together, these data suggest that children who had a period of asymmetric hearing in early life continue to have aural preference for a “better ear” which could compromise development of binaural/spatial hearing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".