Evaluating visual outcomes using optical coherence tomography (OCT) in pediatric multiple sclerosis and other neuroinflammatory conditions
Bibliographic record
Abstract
Optical coherence tomography (OCT) is a technology that is widely used to assess structural abnormalities in the retina for a variety of pediatric conditions. The introduction of this instrument has allowed for widespread access to minimally invasive standardized, reproducible quantified structural assessments of the optic nerve and retina. This has had important implications in pediatric optic neuropathies, populations in whom monitoring of disease activity is essential to making treatment decisions. OCT has had particular relevance for inflammatory optic neuropathies, as onset of an inflammatory optic neuropathy may herald the onset of a chronic inflammatory disorder of the central nervous system (CNS) such as multiple sclerosis, neuromyelitis optica spectrum disorder (aquaporin 4 antibody positive), and myelin oligodendrocyte glycoprotein (MOG) associated disorders. This paper will focus on the application of OCT technology to this group of disorders in pediatrics. After reviewing pediatric-specific anatomic and practical issues pertinent to OCT, we will review knowledge related to the use of OCT in inflammatory pediatric optic neuropathies, with a focus on structural outcomes and their correlation with functional outcome metrics.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".