Optic nerve sheath enhancement on orbital MRI in giant cell arteritis
Bibliographic record
Abstract
BACKGROUND: Differentiating arteritic anterior ischaemic optic neuropathy (A-AION) due to giant cell arteritis (GCA) from non-arteritic anterior ischaemic optic neuropathy (NA-AION) may pose a diagnostic challenge. Our study aimed to assess the use of standard orbital MRI in distinguishing ocular manifestations of GCA from NA-AION. METHODS: This study included 25 consecutive patients (11 GCA, 14 NA-AION) who underwent contrast-enhanced orbital MRIs within 3 months of symptom onset. Two radiologists blinded to clinical data independently evaluated MRIs for the enhancement of the optic nerve sheath (ONS) and other orbital structures. RESULTS: On orbital MRI, ONS enhancement of at least one eye was more common in patients with GCA than NA-AION (64% vs 14%, p=0.02). ONS enhancement on MRI was seen in patients with typical ophthalmologic exam findings of A-AION as well as in GCA patients with other features of ocular ischaemia (eg, retinal artery occlusion). Among patients with GCA, ONS enhancement was bilateral in six of seven cases even when visual symptoms and signs were unilateral. CONCLUSION: Patients with ocular GCA are more likely to have ONS enhancement on MRI compared with NA-AION. ONS enhancement was observed in (i) A-AION and other forms of ocular ischaemia, demonstrating the potential value of MRI in multiple orbital pathologies in GCA, and (ii) both the affected and unaffected eye, suggesting MRI may detect early subclinical ocular disease in GCA. These results highlight the potential value of adding orbital MRI to the diagnostic workup of ocular GCA.
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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) | 0.000 | 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".