Extraocular muscle enlargement in dysthyroid optic neuropathy
Bibliographic record
Abstract
Objective To investigate extraocular muscle volumes in thyroid eye disease (TED) patients with and without dysthyroid optic neuropathy (DON). Design Retrospective cohort study. Participants TED patients who had computed tomography of the orbits. Methods The extraocular muscles were manually segmented in consecutive axial and coronal slices, and the volume was calculated by summing the areas in each slice and multiplying by the slice thickness. Data were collected on patient demographics, disease presentation, thyroid function tests, and antibody levels. Results Imaging from 200 orbits was evaluated. The medial rectus, lateral rectus, superior muscle group, inferior rectus, and superior oblique volumes were significantly greater in orbits with DON compared with TED orbits without DON ( p < 0.01 for all). There was no significant difference in the inferior oblique muscle volume ( p = 0.19). Increase in volume of the superior oblique muscle showed the highest odds for DON. Each 100 m 3 increase in superior oblique, lateral rectus, inferior rectus, medial rectus, and superior muscle group volume was associated with 1.58, 1.25, 1.20, 1.16, and 1.14 times increased odds of DON. Conclusion All extraocular muscle volumes except for the inferior oblique were significantly greater in DON patients. Superior oblique enlargement was associated with the highest odds of DON, suggesting superior oblique enlargement to be a novel marker of DON.
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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 | 0.001 |
| 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) | 0.002 | 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".