Spectral-Domain Optical Coherence Tomography of Retinal Nerve Fiber Layer Thickness in NMO Patients
Bibliographic record
Abstract
BACKGROUND: Neuromyelitis optica (NMO) is a demyelinating syndrome of the central nervous system. NMO might be underdiagnosed at early stages when patients have not yet developed the full spectrum of disease. The aim of this study was to analyze the retinal nerve fiber layer (RNFL) with optical coherence tomography (OCT) and to compare RNFL measurements between NMO patients, patients with relapsing-remitting multiple sclerosis (RRMS), and healthy controls to determine whether differences in RNFL thickness could be an early diagnostic marker for NMO. METHODS: In a cross-sectional study, eyes of 25 NMO patients, 25 RRMS patients, and 50 healthy controls underwent RNFL measurements by OCT. Clinical parameters were collected by history and chart review. Pairwise Wilcoxon rank sum tests with Holm correction were used to compare means of RNFL thickness among 6 groups (NMO, RRMS, and healthy control) of patients [without or with 1 or more episode of optic neuritis (ON)]. The association between RNFL thickness and patient characteristics for NMO group was examined via linear mixed-effects models (adjusting for within-patient intereye correlations and history of ON, where appropriate). RESULTS: Based on the pairwise Wilcoxon rank sum tests with Holm correction, significant differences were found between NMO with 1 episode of ON and non-ON eyes (mean RNFL 63.7 vs 97.0 µm, P < 0.0001), multiple sclerosis (MS) non-ON eyes, and controls (RNFL 93.2 vs 98.4 µm, P = 0.03). No significant differences were found between NMO and MS with 1 attack of ON eyes (RNFL 63.7 vs 73.9 µm, P = 0.46), NMO non-ON eyes and healthy controls (RNFL 97.0 vs 98.4 µm, P = 0.56), and NMO non-ON and MS non-ON (RNFL 97.0 vs 93.2 µm, P = 0.56). For NMO group, RNFL thickness was associated with a history of ON (P < 0.001) but not with disability or disease duration when adjusting for the history of ON (P > 0.1). CONCLUSIONS: RNFL in NMO is not different enough to distinguish NMO ON from MS ON eyes, but the intereye difference in RFNL with a history of unilateral ON may be a better diagnostic marker for NMO.
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.
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 | 0.000 | 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) | 0.001 | 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".