Quantifying visual pathway axonal and myelin loss in neuromyelitis optic (P4.015)
Bibliographic record
Abstract
OBJECTIVE: To determine how optical coherence tomography (OCT) measures reflecting the health of neurons (total macular volume, TMV) and axons (retinal nerve fibre layer, RNFL) relate to MRI-derived measures of myelin (myelin water fraction, MWF) in the optic radiation (OR) of individuals with neuromyelitis optica (NMO) compared to healthy controls. BACKGROUND: NMO is an autoimmune disorder characterized by episodes of inflammation and damage to astrocytes that can result in optic neuritis (ON) and transverse myelitis. The optic nerve is a frequent site for demyelination resulting in visual dysfunction which may be reflected by OCT measures of axonal health and MWF of the OR. DESIGN/METHODS: 8 eyes with ON (NMO¹) and 12 eyes without ON (NMO²) from ten NMO patients (EDSS 2.0-6.0, mean age=43y, 3M/7F) and 24 eyes of twelve healthy subjects (mean age=31y, 3M/9F) were included. OCT assessment involved a Macular Volume protocol (volume of retina in the center 6mm of the macula) and a RNFL Thickness scan (3.4mm ring scan of retinal thickness around optic nerve). The MRI protocol included a 32-echo T2-relaxation GRASE sequence. Average MWF values were calculated within the OR. RESULTS: RNFL thickness (NMO¹:70.5±17.9μm; NMO²:91.3±8.7μm; controls:102.9±15.0μm) was reduced in NMO¹ compared to NMO² (p=0.01) and controls (p<0.0001). TMV (NMO¹:7.9±0.4mm³; NMO²:8.4±0.4mm³; controls:8.9±0.4mm³) was reduced in NMO¹ compared to NMO² (p=0.02) and controls (p<0.0001). Also NMO² had reduced TMV compared to controls (p=0.02). Decreased OR MWF was also observed in NMO (NMO:0.098±0.01; controls:0.11±0.01, p=0.02). There was a strong correlation between the OR MWF and RNFL (r=0.50,p=0.003) and TMV (r=0.60,p=0.0002). CONCLUSIONS: The correspondence between reductions in OCT measures of neuronal and axonal health in the anterior visual pathway and MRI-based measures of myelin health in the posterior visual pathway suggests that these measures may be used to evaluate disease progression and treatment approaches that promote repair.
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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 | 0.001 |
| 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".