Predominant Contribution of Superior Rectus–Levator Complex Enlargement to Optic Neuropathy and Inferior Visual Field Defects in Thyroid Eye Disease
Bibliographic record
Abstract
PURPOSE: To compare extraocular muscle volumes in thyroid eye disease patients with and without compressive optic neuropathy. METHODS: A retrospective review of 44 orbital CT scans (28 orbits without compressive disease and 16 orbits with compressive optic neuropathy) was conducted. The extraocular muscle volumes, summated soft tissue volumes, and optic nerve volumes were calculated at a section in the posterior 1/3 of the orbit. The visual fields of the orbits with compressive optic neuropathy were analyzed. RESULTS: The mean combined extraocular muscle/summated soft tissue volume ratio and the mean superior rectus-levator complex/summated soft tissue volume ratio were greater in those with compressive optic neuropathy than in those without compressive optic neuropathy (p = 0.02, 0.008, respectively). The ratio of the mean inferior, medial, or lateral rectus/summated soft tissue volume did not differ significantly between patients with or without compressive optic neuropathy (p values of 0.315, 0.615, and 0.254, respectively). Visual field analysis of the compressive optic neuropathy group demonstrated that 58% of the orbits with visual field defects had inferior field defects. CONCLUSIONS: When measured at a section near the orbital apex, the mean combined muscle/summated soft tissue volume ratio and the mean superior rectus-levator complex/summated soft tissue volume ratio are greater in those with compressive disease than those without. This suggests that the specific enlargement of the superior rectus-levator complex makes a significant contribution to thyroid eye disease-compressive optic neuropathy and may explain the inferior visual field deficits classically found in this group of patients.
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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 | 0.001 |
| 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) | 0.002 | 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".