P.018 Oculomotor learning as a biomarker in Huntington’s Disease (HD) patients
Bibliographic record
Abstract
Background: Huntington’s disease (HD) is an inherited neurodegenerative disorder associated with cognitive, psychiatric, and motor dysfunction. As a potential behavioural biomarker, experimental tasks assessing motor learning may thus provide a reasonable assay of HD onset and progression. The saccadic adaptation paradigm is a non-invasive, accessible method of assessing rapid learning in the oculomotor system. Evidence demonstrates that the thalamus and basal ganglia are important loci for saccadic adaptation, also known to exhibit neurodegenerative pathology before the onset of clinically observable symptoms in HD. Methods: 26 early symptomatic HD patients (Total Functional Capacity Score ≥ 10/13) and sex/age-matched controls were tested on a standard saccadic adaptation task. Eye movements were measured using infrared oculography. Learning dynamics of how quickly the participants adapted their saccade metrics were analysed using state learning models. Results: Initial findings demonstrate that the learning dynamics of HD patients are slower and more variable with respect to saccade amplitude compared to controls. Conclusions: These results demonstrate that motor learning dynamics as captured by a saccadic adaptation task reveal early motor dysfunction in HD, thus providing a discriminating tool to detect early pathological changes in HD patients. Further work is needed regarding applications as a biomarker for 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".