Longitudinal Study of Retinal Nerve Fiber Layer Thickness and Macular Volume in Patients With Neuromyelitis Optica Spectrum Disorder
Bibliographic record
Abstract
BACKGROUND: Neuromyelitis spectrum disorder (NMOSD) is a rare autoimmune disorder previously thought to be a subtype of multiple sclerosis (MS). NMOSD is characterized by episodes of inflammation and damage to astrocytes that primarily results in damage to optic nerve and spinal cord. The objective of this exploratory study was to use optical coherence tomography (OCT) to measure axonal and neuronal health in NMOSD eyes over time. METHODS: Nine patients with definite NMOSD were assessed at baseline and follow-up visits (time between visits: 35-55 months). OCT assessment involved a macular volume protocol and a retinal nerve fiber layer (RNFL) thickness scan. RESULTS: The temporal, inferior, nasal, or superior quadrant and the mean global RNFL thickness, macular thickness, and volume of each NMOSD patient was unchanged compared with baseline for each eye separately and both together. There also was no change between the 2 time points for the OCT measures for eyes affected and unaffected by optic neuritis and all eyes together except for a significant change in the temporal RNFL quadrant when all NMOSD eyes were pooled together (mean = 2.88 μm, SD = 3.7, P = 0.021). CONCLUSIONS: Unlike in MS eyes, ongoing RNFL and macular thinning secondary to brain and optic nerve atrophy could not be observed in NMOSD eyes during an observation period of 4 years. This might be an additional marker to distinguish these 2 diseases. However, to confirm this finding, more long-term data are needed to compare these 2 diseases longitudinally.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".