Tracking retinal nerve fiber layer loss after optic neuritis: a prospective study using optical coherence tomography
Bibliographic record
Abstract
INTRODUCTION: Optic neuritis causes retinal nerve fiber layer damage, which can be quantified with optical coherence tomography. Optical coherence tomography may be used to track nerve fiber layer changes and to establish a time-dependent relationship between retinal nerve fiber layer thickness and visual function after optic neuritis. METHODS: This prospective case series included 78 patients with optic neuritis, who underwent optical coherence tomography and visual testing over a mean period of 28 months. The main outcome measures included comparing inter-eye differences in retinal nerve fiber layer thickness between clinically affected and non-affected eyes over time; establishing when RNFL thinning stabilized after optic neuritis; and correlating retinal nerve fiber layer thickness and visual function. RESULTS: The earliest significant inter-eye differences manifested 2-months after optic neuritis, in the temporal retinal nerve fiber layer. Inter-eye comparisons revealed significant retinal nerve fiber layer thinning in clinically affected eyes, which persisted for greater than 24 months. Retinal nerve fiber thinning manifested within 6 months and then stabilized from 7 to 12 months after optic neuritis. Regression analyses demonstrated a threshold of nerve fiber layer thickness (75 microm), which predicted visual recovery after optic neuritis. CONCLUSIONS: Retinal nerve fiber layer changes may be tracked and correlated with visual function within 12 months of an optic neuritis event.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".