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
Older Adults Driving Studies
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.

1,131 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.
1,131 works in the cohort · of 4,299,418page 16 of 23

Labels cover 8 of 1,131 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 1,131 of 1,131 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.

aboutno affunlabeled
Reading and driving
Vincent Hanlon
2003· article· en· Europe PMC (PubMed Central)· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
3D-MOT predicts driving skills in older drivers
Jesse Michaels, Donald H. Watanabe, Pierro Hirsch, François Bellavance, Jocelyn Faubert
2016· article· en· Investigative Ophthalmology & Visual Science· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Poster 57
Barbara Mazer, Isabelle Gélinas, Marie T. Vanier, Josée Duquette, Constant Rainville, James A. Hanley
2005· article· en· Archives of Physical Medicine and Rehabilitation· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
aboutno affunlabeled
Canadian Certificates for a New Prosperity
2014· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affgemma · no categorygpt · no categorymodels agree
Driving ability after conscious sedation: a systematic review
Matteo Melini, Francesco Cavallin, Andrea Forni, Matteo Parotto, Gastone Zanette
2025· review· en· Minerva Dental and Oral Science· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Heart Failure and Fitness to Drive
David B. Carr, Brian R. Ott
2020· letter· en· Journal of Cardiac Failure· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Urban practitioner vignette
Marianne Wilkat, Barry Pendergast, Natalie S. Channer
2021· book-chapter· fr· Policy Press eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Transportation issues in dementia
Mark Rapoport, Andy Hyde, Gary Naglie
2020· book-chapter· en· Policy Press eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About