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 39 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.

affno abstractunlabeled
Artificial Intelligence and Clinical Care
Cary P. Gross, Ilana B. Richman, Nathan M. Stall, Jerard Kneifati‐Hayek, Heather Allore, Eve Rittenberg +11 more
2025· article· en· JAMA Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Artificial Intelligence in psychiatry
Shabbir Amanullah
2023· article· en· Archives of Biological Psychiatry· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Publisher Correction: Advancing ethics review practices in AI research
Madhulika Srikumar, Rebecca Finlay, Grace Abuhamad, Carolyn Ashurst, Rosie Campbell, Emily Campbell-Ratcliffe +5 more
2023· article· en· Nature Machine Intelligence· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
2
citations
affunlabeled
Shadow Program Committee Initiative
Patanamon Thongtanunam, Ayushi Rastogi, Foutse Khomh, Serge Demeyer, Meiyappan Nagappan, Kelly Blincoe +1 more
2021· article· en· ACM SIGSOFT Software Engineering Notes· Medicine
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
2
citations
aboutno affunlabeled
Pitfalls of Evidence-Based AI Policy
Stephen Casper, David Krueger, Dylan Hadfield-Menell
2025· article· en· SuperIntelligence - Robotics - Safety & Alignment· Medicine
machine prediction:candidate · metaresearchconsensus · none
2
citations
affaboutunlabeled
How college students use ChatGPT
Nagham M. Mohammad, Matthew Demers, Erin McCubbin, Jon Mitchell, Sara M. Fulmer
2025· article· Pedagogical Research· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
← previousnext →

How this was built: Screen · Findings · About