Hearing Loss Progression and Contralateral Involvement in Children with Unilateral Sensorineural Hearing Loss
Bibliographic record
Abstract
Objectives We undertook this study to determine the rate of hearing loss progression in the affected ear of children with unilateral sensorineural hearing loss and without an enlarged vestibular aqueduct, and the rate of new-onset hearing loss in the contralateral ear. Methods We searched the database at our pediatric tertiary care center to identify patients who met the inclusion criteria, examining demographic variables, audiometric data, and presumptive causes. Results We identified 198 patients. At presentation, they showed slight left-sided and male predominances. Of 142 patients who had sufficient audiometric follow-up for us to evaluate progression, 21% showed ipsilateral progression and 10.6% developed new-onset hearing loss in the contralateral ear. Isolated high-frequency loss was identified in 11 patients (5.6%), 8 of whom had sufficient follow-up for us to identify progression. Two showed progression; 4 others with progression in the ipsilateral ear developed new-onset high-frequency loss in the contralateral ear. Temporal bone anomalies were identified in 26 children (13%), and these children were more likely to have profound hearing loss than were those without temporal bone anomalies (46% versus 23%). Conclusions The findings suggest that unilateral sensorineural hearing loss may not always be a unilateral process, but that it may be the initial manifestation of bilateral auditory dysfunction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".