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
Topic
Artificial Intelligence in Healthcare and Education
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.

3,498 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.
3,498 works in the cohort · of 4,299,418page 60 of 70

Labels cover 44 of 3,498 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 3,498 of 3,498 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
AI and Medical Education
Alexandra T. Greenhill
2023· other· en· AI in Clinical Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Way Too Early Standings Updates
2021· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
AIs and Hands-on Healthcare Professionals
Eike‐Henner W. Kluge
2024· book-chapter· en· ˜The œInternational library of ethics, law and technology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Use of artificial intelligence in family medicine publications
Sarina Schrager, Dean A. Seehusen, Sumi M. Sexton, Caroline R. Richardson, Jon O. Neher, Nicholas Pimlott +5 more
2025· article· en· Canadian Family Physician· Medicine
machine prediction:candidate · metaresearchconsensus · none
0
citations
aboutno affunlabeled
Town Of Canton 2013 Annual Report
2013· other· en· State Library's electronic repository (State Library of Massachusetts)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Artificial Intelligence in APCS
2023· article· ru· Electronic scientific archive of UrFU (Ural Federal University)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
AI Technologies Driving Wellness Transformation
Soumi Majumder, Nilanjan Dey
2025· book-chapter· en· Studies in computational intelligence· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
The White Foreigner - I Am Not Normal
2014· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About