The PREVENT‐AD cohort: Accelerating Alzheimer's disease research and treatment in Canada and beyond
Bibliographic record
Abstract
The PResymptomatic EValuation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD) is an investigator-driven study that was created in 2011 and enrolled cognitively normal older adults with a family history of sporadic AD. Participants are deeply phenotyped and have now been followed annually for more than 12 years (median follow-up 8.0 years, SD 3.1). Multimodal magnetic resonance imaging (MRI), genetic, neurosensory, clinical, cerebrospinal fluid, and cognitive data collected until 2017 on 348 participants who agreed to open sharing with the neuroscience community were already available. We now share a new release including 6 years of additional follow-up cognitive data, and additional MRI follow-ups, clinical progression, new longitudinal behavioral and lifestyle measures (questionnaires, actigraphy), longitudinal AD plasma biomarkers, amyloid-beta and tau positron emission tomography (PET), magnetoencephalography, as well as neuroimaging analytic measures from all MRI modalities. We describe the PREVENT-AD study, the data shared with the global research community, as well as the model we created to sustain longitudinal follow-ups while also allowing new innovative data collection. HIGHLIGHTS: The PResymptomatic EValuation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD) is a single-site longitudinal study that started in 2011 with annual follow-up data collection on individuals at risk of Alzheimer's disease who were all cognitively normal at enrolment. All 387 participants were enrolled between 2011 and 2017 and 306 (79%) of these participants were still in the study as of December 2023. While the PREVENT-AD dataset was not originally planned to be shared with the global research community, 348 participants retrospectively consented for their data to be shared with researchers worldwide. The first release of data was in 2019. We now share a second release that includes 6 years of additional follow-up visits, information on clinical progression and novel cognitive, behavioral, genetic, plasma and neuroimaging (amyloid and tau positron emission tomography [PET], magnetoencephalography [MEG], and new magnetic resonance imaging [MRI] sequences) data. It also includes analytic outputs for neuroimaging modalities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".