P.028 Eye movement biomarkers for early detection of multiple sclerosis disease progression
Bibliographic record
Abstract
Background: There is growing body of evidence linking abnormal eye movements in people with multiple sclerosis (MS) to disease severity and cognition which could better detect disease progression. The objective of this study is to determine if a novel eye-tracking tool can accurately predict disease severity and cognitive status based on eye movement metrics and characterize how they evolve with progression. Methods: Persons with MS (n=132) will be followed over 4 years with clinical assessments every 6 months. Eye movements are also assessed while performing oculomotor tasks using Innodem Neurosciences’ patented eye-tracking technology. The eye movement metrics will be inputted into machine learning classifying algorithms to identify which metrics can serve as reliable Eye Movement Biomarkers (EMB) for MS progression and cognitive status. Results: There were 16 participants recruited as of January 2022 with mean age 47. 3 (SD 10.4; range 26-67), gender (12F/4M), EDSS 2.59 (SD 1.49; range 1.5-6.5), SDMT 51.4 (SD 14.1, range 24-78). With current enrollment, there is a negative correlation between EDSS and SDMT (r = -0.47) as observed in the literature. Conclusions: This trial will demonstrate the utility of EMBs for monitoring MS progression by improving physicians’ access to a reliable, non-invasive, sensitive and accessible marker of disease progression.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".