MétaCan
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Health, Environment, Cognitive Aging
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,205 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,205 works in the cohort · of 4,299,418page 8 of 25

Labels cover 6 of 1,205 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,205 of 1,205 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
PHYSICAL ACTIVITY AND BRAIN HEALTH
Teresa Liu‐Ambrose
2017· article· en· Innovation in Aging· Environmental Science
machine prediction:candidate · noneconsensus · none
4
citations
afffundaboutunlabeled
An Environmental Scan of Medical Assessment Units in Canada
Dean Yergens, Elizabeth A. Fradgley, Ranjani Aiyar, Eddy Lang, Brian H. Rowe, William A. Ghali
2014· article· en· Healthcare Quarterly· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Precision medicine: Crossing the biomedical scales with AI
Sharat Israni, Gary D. Bader, Sergio E. Baranzini, John A. Capra, Marina Sirota, Christina V. Theodoris +1 more
2025· article· en· The Journal of Precision Medicine Health and Disease· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
fundno affunlabeled
Towards cascading genetic risk in Alzheimer’s disease
André Altmann, Leon Aksman, Neil P. Oxtoby, Alexandra L. Young, Daniel C. Alexander, Frederik Barkhof +3 more
2023· preprint· en· medRxiv· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Key challenges in epidemiology: embracing open science
Edward Xu, Anna Catharina Vieira Armond, David Moher, Kelly D. Cobey
2024· article· en· Journal of Clinical Epidemiology· Environmental Science
machine prediction:candidate · metaresearch+open_scienceconsensus · metaresearch
3
citations
affunlabeled
Cancer et environnement : expertise collective
Isabelle Baldi, Denis Bard, Robert Barouki, Simone Benhamou, Jacques Bénichou, Marie‐Odile Bernier +27 more
2008· preprint· fr· HAL (Le Centre pour la Communication Scientifique Directe)· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Probing the network structure of health deficits in human aging
Spencer G. Farrell, Arnold Mitnitski, Olga Theou, Kenneth Rockwood, Andrew D. Rutenberg
2018· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About