Temporal Bone Findings on Computed Tomography Imaging in Branchio-Oto-Renal Syndrome
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To describe temporal bone findings using visual inspection and direct measurement on computerized tomography (CT) in individuals with branchio-oto-renal syndrome (BOR). We ask if it is possible for the untrained observer to use a battery of CT observations as a tool in the overall evaluation of the BOR phenotype. STUDY DESIGN: Retrospective evaluation of CT findings in individuals with a clinical diagnosis of BOR based on criteria derived from genotype-phenotype analyses. METHODS: Prospective measurement of temporal bone CT imaging in 21 individuals (42 ears) with BOR and 21 normally hearing controls (21 ears) was performed. Thirty-nine aspects of each temporal bone were evaluated: 17 by direct measurement, 5 computed from direct measurement, and 17 by visual inspection. Thirty-eight recordings from each ear were made on axial section and 1 was made on coronal section. RESULTS: Statistically significant differences were found between BOR and control groups in 30 of 39 categories (76.9%). The most common and easily identifiable characteristics of BOR by visual inspection were 1) hypoplastic apical turn of the cochlea, 2) facial nerve deviated to the medial side of the cochlea, 3) funnel-shaped internal auditory canal, and 4) patulous eustachian tube. The embryological origin of temporal bone anomalies in BOR are described. CONCLUSIONS: CT evaluation of the temporal bone, when properly investigated, should be used as an important tool in the overall evaluation of the BOR phenotype.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".