Myosin Mutations and Sudden Sensorineural Hearing Loss: Results of Whole Exome Sequencing
Bibliographic record
Abstract
OBJECTIVE: Idiopathic sudden sensorineural hearing loss (ISSNHL) affects 66,000 patients per year in the United States. Genetic mutations have been associated with progressive hearing loss; however, genetic mutations associated with ISSNHL have not been identified. METHODS: A prospective cohort study of adults older than 18 years presenting with ISSNHL at a tertiary academic medical center. Whole exome sequencing (WES) was conducted using Genome Analysis Toolkit best practices. An automated diagnostic screen employing a variety of models for pathogenicity was conducted across all genes with no specific targets. Candidate pathogenic variants were reviewed by a team of geneticists and clinicians. Variants were crossed-referenced with 92 known hearing loss associated genes. RESULTS: Twenty-nine patients with SSNHL were screened using WES. The average age of patients was 53 ± 17.1 years, and most patients were White (62%) and men (55%). The mean pure tone average was 64.8 ± 31.3 dB for the affected ear. Using a 0.1% allele frequency screen, 12 (41%) cases had a mutation in any of the nine selected myosin genes. When we restrict to singletons (allele frequency = 0%), 21% (n = 6) of cases have qualifying variants, whereas only 3.8% (n = 481) of 12,577 healthy controls carry qualifying variants (p < 0.01). Most mutations (80%) were missense mutations. Of the novel mutations, one was a frameshift mutation, and two were a stop-gained function. Three were missense mutations. CONCLUSION: Myosin mutations may be associated with ISSNHL. However, larger population screening is needed to confirm the association of myosin mutation with ISSNHL and steroid responsiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".