Retinal imaging with optical coherence tomography: a biomarker in multiple sclerosis?
Bibliographic record
Abstract
Multiple sclerosis (MS) is a progressive neurological disorder characterized by both inflammatory and degenerative components that affect genetically susceptible individuals. Currently, the cause of MS remains unclear, and there is no known cure. Commonly used therapies tend to target inflammatory aspects of MS, but may not halt disease progression, which may be governed by the slow, subclinical accumulation of injury to neuroaxonal structures in the central nervous system (CNS). A recognized challenge in the field of MS relates to the need for better methods of detecting, quantifying, and ameliorating the effects of subclinical disease. Simply stated, better biomarkers are required. To this end, optical coherence tomography (OCT) provides highly reliable, reproducible measures of axonal damage and neuronal loss in MS patients. OCT-detected decrements in retinal nerve fiber layer thickness and ganglion-cell layer-inner plexiform layer thickness, which represent markers of axonal damage and neuronal injury, respectively, have been shown to correlate with worse visual outcomes, increased clinical disability, and magnetic resonance imaging-measured burden of disease in MS patients. Recent reports have also suggested that OCT-measured microcystic macular edema and associated thickening of the retinal inner nuclear layer represent markers of active CNS inflammatory activity. Using the visual system as a putative clinical model in MS, OCT measures of neuroaxonal structure can be correlated with functional outcomes to help us elucidate mechanisms of CNS injury and repair. In this review, we evaluate evidence from the published literature and ongoing clinical trials that support the emerging role of OCT in diagnosing, staging, and determining response to therapy in MS patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".