Tracking retinal nerve fiber layer loss after optic neuritis: a prospective study using optical coherence tomography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it