Leveraging Visual Outcome Measures to Advance Therapy Development in Neuroimmunologic Disorders
Bibliographic record
Abstract
The visual system offers unparalleled precision in the assessment of neuroaxonal damage. With the majority of patients with multiple sclerosis (MS) experiencing afferent and efferent visual dysfunction, outcome measures capturing these deficits provide insight into neuroaxonal injury, even in those with minimal disability. Ideal for use in clinical trials, visual measures are generally inexpensive, accessible, and reproducible. Quantification of visual acuity, visual fields, visual quality of life, and electrophysiologic parameters allows assessment of function, whereas optical coherence tomography (OCT) provides reliable measures of the structural integrity of the anterior afferent visual pathway. The technology of oculomotor biometrics continues to advance, and discrete measures of fixation, smooth pursuit, and saccadic eye movement abnormalities are ready for inclusion in future trials of MS progression. Visual outcomes allow tracking of neuroaxonal injury and aid in distinguishing MS from diseases such as neuromyelitis optica spectrum disorder (NMOSD) or myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD). OCT has also provided unique insights into pathophysiology, including the identification of foveal pitting in NMOSD, possibly from damage to Müller cells, which carry an abundance of aquaporin-4 channels. For some study designs, the cost-benefit ratio favors visual outcomes over more expensive MRI outcomes. With the next frontier of therapeutics focused on remyelination and neuroprotection, visual outcomes are likely to take center stage. As an international community of collaborative, committed, vision scientists, this review by the International MS Visual System Consortium (IMSVISUAL) outlines the quality standards, informatics, and framework needed to routinely incorporate vision outcomes into MS and NMOSD trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".