Open science datasets from PREVENT‐AD, a longitudinal cohort of pre‐symptomatic Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background In the last 10 years, the PResymptomatic EValuation of Experimental or Novel Treatments for Alzheimer Disease (PREVENT‐AD) research program has collected both biological and behavioral data longitudinally, using well‐recognized biomarkers, to better understand and prevent Alzheimer’s disease (AD). Our cohort is composed of older individuals with a family history of AD, but who were themselves cognitively unimpaired when enrolled in the study (aged 63 years old ± 5, at entry). To move AD research further, we recently shared this rich dataset with the global research community. Method We created a data sharing model including two levels of access based on data sensitivity and risk of potential re‐identification, framed with a series of usage terms. Preparation of the datasets included selection of the variables to share, quality controls, outlier analysis, de‐identification, and creation of detailed data dictionaries for proper re‐usability. Each research participant was retrospectively re‐consented. Result More than 90% of participants (349 out of 386) agreed to openly share their data. Repositories are accessible openly at https://openpreventad.loris.ca, to qualified researchers at https://registeredpreventad.loris.ca, and through the unified interface of the Canadian Open Neuroscience Platform. The shared data collected from 2012 to 2020 (Table 1) includes longitudinal multimodal magnetic resonance imaging, gene variants, neuropsychological assessments, neurosensory evaluations, subjective cognitive decline information, as well as multiple behavioral data (e.g. sleep quality, neuropsychiatric factors, personality traits, etc). Amyloid and tau measurements from cerebrospinal fluid (n=106; longitudinal) and positron emission tomography (n = 130) are also available on subsamples of participants. To date, more than 250 users have accessed the PREVENT‐AD datasets. Conclusion Creation of open datasets from sensitive human data requires technical, human, and financial investments. The challenge remains important as we must constantly deploy our sharing initiative efforts as new data is collected, and new modalities incorporated in the cohort. By offering this evolving resource to the research community, we aim to acknowledge the implication of our research participants by expanding the potential of the data they generously provided, generate a higher rate of new discoveries in AD pathogenesis, and contribute to the international efforts towards prevention of AD.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".