Optical coherence tomography measures correlate with brain and spinal cord atrophy and multiple sclerosis disease‐related disability
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Both optical coherence tomography (OCT) and magnetic resonance imaging (MRI) volumetric measures have been postulated as potential biomarkers of multiple sclerosis (MS)-related disability. The aim of the study was to investigate the association between OCT and brain volume and spinal cord area (SCA) parameters in patients with relapsing MS and to assess their independent associations with disability. METHODS: This was a cross-sectional analysis of 90 patients with MS who underwent OCT and MRI examination. Values of peripapillary retinal nerve fibre layer (pRNFL), ganglion cell/inner plexiform layer (GCIPL) and inner nuclear layer of eyes without previous optic neuritis were obtained. SCA and brain parenchymal fraction (BPF), grey and white matter fractions were obtained. Multivariable regression analyses were conducted with disability as dependent variable. RESULTS: Lower pRNFL thickness and lower GCIPL volume as well as lower BPF, grey matter fraction and SCA were associated with a longer disease duration and a higher Expanded Disability Status Scale score. Lower pRNFL thickness and GCIPL volumes were associated with lower BPF and SCA. In the multivariable logistic regression analyses, pRNFL thickness and GCIPL volume outperformed MRI in predicting disability. CONCLUSIONS: The OCT measures correlate with brain and spinal cord atrophy and appear more closely associated with disability than MRI volumetric measures.
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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 | 0.003 |
| 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) | 0.002 | 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".