The Ontario Neurodegenerative Disease Research Initiative
Bibliographic record
Abstract
Abstract Objective In individuals over the age of 65, concomitant neurodegenerative pathologies contribute to cognitive and/or motor decline and can be aggravated by cerebrovascular disease, but our understanding of how these pathologies synergize to produce the decline represents an important knowledge gap. The Ontario Neurodegenerative Disease Research Initiative (ONDRI), a multi-site, longitudinal, observational cohort study, recruited participants across multiple prevalent neurodegenerative diseases and cerebrovascular disease, collecting a wide array of data and thus allowing for deep investigation into common and unique phenotypes. This paper describes baseline features of the ONDRI cohort, understanding of which is essential when conducting analyses or interpreting results. Methods Five disease cohorts were recruited: Alzheimer’s disease/amnestic mild cognitive impairment (AD/MCI), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and cerebrovascular disease (CVD). Assessment platforms included clinical, neuropsychology, eye tracking, gait and balance, neuroimaging, retinal imaging, genomics, and pathology. We describe recruitment, data collection, and data curation protocols, and provide a summary of ONDRI baseline characteristics. Results 520 participants were enrolled. Most participants were in the early stages of disease progression. Participants had a median age of 69 years, a median Montreal Cognitive Assessment score of 25, a median percent of independence of 100 for basic activities of daily living, and a median of 93 for instrumental activities. Variation between disease cohorts existed for age, level of cognition, and geographic location. Conclusion ONDRI data will enable exploration into unique and shared pathological mechanisms contributing to cognitive and motor decline across the spectrum of neurodegenerative diseases.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".