The relation of eye movements to the occurrence of freezing of gait in Parkinson’s disease
Bibliographic record
Abstract
Freezing of gait is a debilitating motor symptom in Parkinson's disease that significantly increases fall risk and impairs quality of life. The poorly understood pathophysiology of freezing of gait presents challenges for early prediction and therapeutic intervention. This prospective study investigated whether eye movement abnormalities, specifically in the anti-saccade paradigm, could predict freezing of gait onset in Parkinson's disease patients over a two-year follow-up period. We analysed longitudinal data from the Ontario Neurodegenerative Disease Research Initiative, focusing on Parkinson's disease patients without freezing of gait at baseline who underwent comprehensive clinical evaluations and eye movement recordings. Anti-saccade reaction time and error ratio, combined with clinical measures including right upper extremity rigidity, demonstrated significant predictive value for freezing of gait development within two years. These findings suggest that eye movement deficits and upper limb rigidity emerge years before freezing of gait onset, indicating a prodromal phase in freezing of gait pathogenesis. The predictive relationship between these measures supports the hypothesis of shared neural substrates, potentially involving the mesencephalic locomotor region, in the development of both oculomotor dysfunction and gait freezing episodes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".