COMPASS‐ND: A unique cohort across the dementia spectrum
Bibliographic record
Abstract
BACKGROUND: The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) used wide inclusion criteria recruiting participants across the spectrum from normal cognition to cognitive impairment to all types of clinical dementia, including mixed dementia. 1173 participants comprising 11 diagnostic cohorts, completed screening, clinical, neuropsychology, sensory and motor assessments, magnetic resonance imaging (MRI) and provided biomarker bio-samples. METHOD: Recruitment was from advertising and specialty clinics for neurocognitive disorders and/or memory impairment. Robust data monitoring and cleaning occurred on the baseline and Time 2 data. Genetic, MRI, Cerebrospinal Fluid (CSF) and the majority of blood biomarkers analyses have been completed. RESULT: Data releases uploaded to the Longitudinal Online Research and Imaging System (LORIS) include alpha numeric baseline data, neuroimaging analyses, CSF, genetic and blood biomarkers. 40 autopsies have been completed. 119 data access requests have been submitted to the Data Access Subcommittee. All returning participants will repeat Time 3 extensive clinical and neuropsychology assessments, MRI and provide bio-samples. Phase III new recruitment will include 400 diverse participants, with up to Grade 12 education, who are not cognitively impaired, or who have cognitive impairment, but not dementia. The balance of each cohort for sex/gender will be a consideration. CONCLUSION: COMPASS-ND fills a knowledge gap by recruiting participants representing the full spectrum of neurodegeneration. This distinguishes COMPASS-ND from other large-scale studies. Challenges and strategies for engagement and research site level funding will be discussed. Data are available to registered researchers and trainees in Canada and around the world through data access requests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".