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Record W4412649920 · doi:10.1101/2025.07.22.25331791

The PREVENT-AD cohort: accelerating Alzheimer’s disease research and treatment in Canada and beyond

2025· preprint· en· W4412649920 on OpenAlexafffundabout
Sylvia Villeneuve, Judes Poirier, John C.S. Breitner, Jennifer Tremblay‐Mercier, Jordana Remz, Jean‐Michel Raoult, Yara Yakoub, Jobst Rudolf, Ting Qiu, Alfonso Fajardo Valdez, Béry Mohammediyan, Mohammadali Javanray, Amelie Metz, Safa Sanami, Valentin Ourry, Alfie Wearn, Manon Edde, Julie Gonneaud, Cherie Strikwerda‐Brown, Christine Tardif, Claudine Gauthier, Maxime Descoteaux, Mahsa Dadar, Étienne Vachon‐Presseau, Andrée‐Ann Baril, Simon Ducharme, Maxime Montembeault, Maiya R. Geddes, Jean‐Paul Soucy, Natasha Rajah, Robert Laforce, Christian Bocti, Christos Davatzikos, Pierre Bellec, Pedro Rosa‐Neto, Sylvain Baillet, Alan C. Evans, D. Louis Collins, M. Mallar Chakravarty, Kaj Blennow, Henrik Zetterberg, R. Nathan Spreng, Alexa Pichet Binette

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalMontreal Heart InstituteUniversité de SherbrookeUniversité LavalUniversité de MontréalConcordia UniversityHôpital du Sacré-Cœur de MontréalMcGill UniversityDouglas Mental Health University InstituteUniversity of TorontoMontreal Neurological Institute and Hospital
FundersNational Institute on AgingCanada First Research Excellence FundCanadian Institutes of Health ResearchAlzheimer SocietyNational Institutes of HealthGovernment of CanadaPfizerFondation Brain CanadaMcGill UniversityPfizer CanadaAlzheimer's Association
KeywordsDiseaseCohortMedicineAlzheimer's diseaseDementiaGerontologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

The 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 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 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.328
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes3
Has abstractyes

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