Is CT or MRI the optimal imaging investigation for the diagnosis of large vestibular aqueduct syndrome and large endolymphatic sac anomaly?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We explored whether there was a difference between measurements obtained with CT and MRI for the diagnosis of large vestibular aqueduct syndrome or large endolymphatic sac anomaly, and whether this influenced diagnosis on the basis of previously published threshold values (Valvassori and Cincinnati). We also investigated whether isolated dilated extra-osseous endolymphatic sac occurred on MRI. Secondary objectives were to compare inter-observer reproducibility for the measurements, and to investigate any mismatch between the diagnoses using the different criteria. MATERIALS/METHODS: Subjects diagnosed with large vestibular aqueduct syndrome or large endolymphatic sac anomalies were retrospectively analysed. For subjects with both CT and MRI available (n = 58), two independent observers measured the midpoint and operculum widths. For subjects with MRI (± CT) available (n = 84), extra-osseous sac widths were also measured. Results There was no significant difference between the width measurements obtained with CT versus MRI. CT alone diagnosed large vestibular aqueduct syndrome or large endolymphatic sac anomalies in 2/58 (Valvassori) and 4/58 (Cincinnati), whilst MRI alone diagnosed them in 2/58 (Valvassori). There was 93% CT/MRI diagnostic agreement using both criteria. Only 1/84 demonstrated isolated extra-osseous endolymphatic sac dilatation. The MRI-based LVAS/LESA diagnosis was less dependent on which criteria were used. Midpoint measurements are more reproducible between observers and between CT/MR imaging modalities. CONCLUSION: Supplementing MRI with CT results in additional diagnoses using either criterion, however, there is no net increased diagnostic sensitivity for CT versus MRI when applying the Valvassori criteria. Isolated enlargement of the extra-osseous endolymphatic sac is rare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".