Retinal nerve fiber layer thickness in benign multiple sclerosis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Retinal nerve fiber layer (RNFL) thickness has been linked to brain atrophy in multiple sclerosis (MS). However, little is known about retinal atrophy in 'benign' MS. We compared RNFL thickness in benign MS with healthy controls. METHODS: Patients with benign MS (Expanded Disability Status Scale (EDSS) ≤ 3; ≥15 years' disease duration), identified through the British Columbia MS database, along with age-matched healthy controls, were recruited. RNFL thickness was measured using spectral-domain optical coherence tomography. Analysis of variance (ANOVA) was used to compare groups. The association between RNFL thickness and MS patient characteristics was examined via linear mixed-effects models (adjusting for within-patient inter-eye correlations and history of optic neuritis (ON), where appropriate). RESULTS: Overall, 29 benign MS patients and 29 healthy controls were included, totaling 116 eyes. RNFL thickness was lowest for the benign MS eyes, with and then without a history of ON, followed by healthy controls (mean=73.2 µm, SD ± 0.4; 89.9 µm, SD ± 12.5; 96.7 µm, SD ± 10.4; p<0.02). RNFL thickness was associated with a history of ON (p<0.0001), but not EDSS or disease duration (p>0.1). CONCLUSIONS: RNFL thickness was lower in patients with benign MS than healthy controls, regardless of the previous history of ON. However, no association was found between RNFL values and disability or MS disease duration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".