Sex-specific differences in retinal nerve fiber layer thinning after acute optic neuritis
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this study was to explore the potential influence of gender on recovery from optic neuritis (ON) by determining whether differences in retinal nerve fiber layer (RNFL) thickness can be detected between men and women 6 months after an ON event. METHODS: In this prospective cohort study, 39 men and 105 women with acute ON underwent repeat visual and optical coherence tomography (OCT) testing. The main outcome measures were change in RNFL measurements for male and female patients 6 months after ON. RESULTS: Men were older (mean age = 39 years) than women (35 years) (p = 0.05) in this study, and more men (62%) than women (41%) had a diagnosis of relapsing-remitting multiple sclerosis (MS) (p = 0.02). Because age and MS subtype were 2 significant covariates, both variables were controlled for in multiple regression analyses. Other covariates controlled for in the multivariate regression included disease duration (years), use of disease-modifying therapy (yes/no), and use of high-dose corticosteroids for acute ON (yes/no). After 6 months, mean RNFL values were lower in men (74 μm) than women (91 μm) (p < 0.001). Men showed more apparent change in RNFL thickness in their ON eyes from baseline to 6 months after ON than women (p = 0.003). CONCLUSIONS: There may be differences in recovery between men and women after ON, which can be difficult to detect with conventional visual testing. Our findings raise interesting questions about the potential influence of gender in MS, which may be explored in future studies.
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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 | 0.002 |
| 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".