MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
PubMed
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.

23,401 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.
23,401 works in the cohort · of 4,299,418page 391 of 469

Labels cover 41 of 23,401 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 23,401 of 23,401 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 abstractunlabeled
Legends in urology.
Mostafa Elhilali
2012· article· en· PubMed· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Pioneers in Enteric Infections.
Deepti Kulkarni, Anjali Shetty, Rajeev Soman
2015· article· en· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
GRACE under fire.
Sheena Stewart
2016· article· en· PubMed· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
[no title]
Françoise Gendron, Jocelyn Bérubé, Édith Guilbert, Mathieu Lebœuf, Sylvie Ouellet, Catherine Risi +4 more
2018· article· fr· PubMed
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutno abstractunlabeled
Choosing Wisely Canada recommendations.
Donna Campbell
2017· other· en· PubMed· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
President's message. Cradle to grave.
Tom Smith‐Windsor
2016· editorial· fr· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Legends in urology.
C. Chaussy
2010· article· en· PubMed· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Nutrition in Quebec
J E Sylvestre
2010· article· en· PubMed· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Non-DVM wages and trends across Canada.
Chris Doherty
2017· article· en· PubMed· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Cruelty to Animals.
Robert J Evenson
2017· letter· en· PubMed· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About