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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".