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
Journal of Clinical Epidemiology
Topic
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.

1,510 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.
1,510 works in the cohort · of 4,299,418page 5 of 31

Labels cover 96 of 1,510 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 1,510 of 1,510 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.

afffundunlabeled
CONSORT extension for reporting N-of-1 trials (CENT) 2015 Statement
Sunita Vohra, Larissa Shamseer, Margaret Sampson, Cecilia Bukutu, Christopher H. Schmid, Robyn Tate +15 more
2015· article· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
142
citations
affno abstractunlabeled
Forming research questions
R. Brian Haynes
2006· article· en· Journal of Clinical Epidemiology· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
118
citations
affunlabeled
“How-to”: scoping review?
Danielle Pollock, Catrin Evans, Romy Menghao Jia, Lyndsay Alexander, Dawid Pieper, Érica Brandão de Moraes +5 more
2024· article· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
115
citations
afffundno abstractunlabeled
Clinical epidemiology
David L. Sackett
2002· article· en· Journal of Clinical Epidemiology· Medicine
machine prediction:candidate · noneconsensus · none
112
citations
affno abstractunlabeled
When should an effective treatment be used?
John C. Sinclair, Richard J. Cook, Gordon H Guyatt, Stephen G. Pauker, Deborah J. Cook
2001· article· en· Journal of Clinical Epidemiology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
112
citations

How this was built: Screen · Findings · About