Diagnosis of Superior Semicircular Canal Dehiscence in the Presence of Concomitant Otosclerosis
Bibliographic record
Abstract
OBJECTIVE: To review three patients with concurrent otosclerosis and superior canal dehiscence identified before operative intervention and provide a practical diagnostic approach to this clinical scenario. STUDY DESIGN: Retrospective patient series. SETTING: Tertiary/quaternary referral center. PATIENTS: Individuals with confirmed diagnoses of concurrent otosclerosis and superior semicircular canal dehiscence syndrome. INTERVENTIONS: Detailed history and physical examinations were performed on these patients, as well as detailed audiovestibular testing and computed tomography imaging. MAIN OUTCOME MEASURES: Establishing a clear diagnosis of concurrent otosclerosis and superior semicircular canal dehiscence syndrome using a thorough diagnostic approach. RESULTS: Three patients presented with conductive hearing loss and normal tympanic membranes. When history and physical examination yielded suspicious third window symptoms/signs, more detailed audiovestibular testing and computed tomography scan imaging were performed. All three patients were ultimately identified to have concurrent otosclerosis and superior canal dehiscence. Conservative management was the option of choice for two of these patients (trial of a hearing aid) and surgical intervention was performed to treat the otosclerosis in the remaining patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".