Optic nerve sheath measurement to monitor disease activity in giant cell arteritis: a pilot study
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: Optic nerve sheath (ONS) enhancement using magnetic resonance imaging of the orbits was observed in patients with giant cell arteritis (GCA). We previously showed that ONS diameter (ONSD) by bedside ultrasound is increased in patient with active GCA. This study aims to assess whether ONSD decreases with clinical remission in patients with GCA. METHODS: A prospective cohort study was conducted from June 2022 to January 2023. Patients who had an optic nerve ultrasound at GCA diagnosis as part of a previous crosssectional study were eligible. Optic nerve ultrasound was performed by the same investigator at diagnosis and month 3. ONSD (includes the optic nerve and its sheath) and optic nerve diameter (OND) were measured. Descriptive statistics for baseline characteristics and paired sample t-test were performed to assess the mean difference in OND and ONSD between diagnosis and month 3. RESULTS: Nine patients with GCA were included. The median age at disease onset was 79 years (interquartile range (IQR) of 79-82 years), and 7 patients were males. All patients were in clinical remission at month 3 on prednisone (median dose of 15 mg/day, IQR of 10-25 mg). The mean ONSD was lower at month 3 (3.76 mm) compared to baseline (5.98 mm), with a paired mean difference of 2.22 mm (95% CI 1.41-3.03 mm, p < 0.001). As anticipated, OND measurements did not vary between diagnosis and month 3. CONCLUSION: ONSD on ultrasound improves after 3 months of therapy in patients with GCA. A longer prospective study is required to determine if ONSD is useful to assess disease activity in GCA. Key Points • ONS ultrasound can identify patients with active GCA. • The ONSD on ultrasound is dynamic and improved after 3 months of GCA therapy. • ONS ultrasound may be useful to monitor disease activity in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".