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
Primary Care and Health Outcomes
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.

4,685 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.
4,685 works in the cohort · of 4,299,418page 40 of 94

Labels cover 32 of 4,685 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 4,685 of 4,685 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.

affno abstractgemma · stsgpt · no categorymodels split
The path of Chile towards the institutionalisation of evidence-based health policy
Paula García-Celedón, Deborah Navarro-Rosenblatt, Carolina Ibarra-Castillo, Lucy Kuhn-Barrientos, Cristián Mansilla, Dino Sepúlveda
2025· article· en· BMJ evidence-based medicine· Health Professions
machine prediction:candidate · metaresearchconsensus · none
2
citations
affunlabeled
Reply to Drs. Endersby et al
De Q.H. Tran, Roderick J. Finlayson
2012· article· en· Regional Anesthesia & Pain Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
affvenueaboutunlabeled
Is Canada Ready to Partner for Value-Based Healthcare?
Jason Vanderheyden, Gabriela Prada
2020· article· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
venueaboutno affunlabeled
Incentives Required to Drive Change
Stephen Corbett
2012· letter· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
La prescription sociale
Dominik Alex Nowak, Kate Mulligan
2021· article· en· Canadian Family Physician· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Marketing family medicine.
Sarah Kredentser
2009· article· en· PubMed· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
venueaboutno affunlabeled
Family medicine’s stress test
Nicholas Pimlott
2022· letter· en· Canadian Family Physician· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
affvenueaboutunlabeled
Survey aims to capture patient experience
Brian Owens
2015· article· en· Canadian Medical Association Journal· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About