Exploring the Association between Retinal Nerve Fiber Layer Thickness and Initial Magnetic Resonance Imaging Findings in Patients with Acute Optic Neuritis
Bibliographic record
Abstract
Background. Recent studies have shown that OCT-measured retinal nerve fiber layer (RNFL) values may represent a marker for axonal damage in the anterior visual pathway of optic neuritis (ON) and multiple sclerosis (MS) patients. The goal of this study was to determine the link between RNFL values and initial magnetic resonance imaging (MRI) evidence of central nervous system (CNS) inflammation in patients with acute ON. Methods. Fifty patients who experienced ON as a clinically isolated syndrome (CIS) were followed for a mean period of 34 months with OCT testing. RNFL values in affected (ON) eyes and clinically unaffected (non-ON) eyes were compared between patients with MRI evidence of white matter lesions and those with normal baseline MRI findings, over a two year period. Findings. Twenty-one patients (42%) developed clinically definite MS (CDMS) during the study. After two years, temporal RNFL values were thinner (P = .07) in ON patients with MRI lesions at baseline, but the results were not significant. Conclusions. There is no association between RNFL values and baseline MRI status in ON patients at risk for future CDMS over a two year period.
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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 | 0.003 |
| 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) | 0.001 | 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".