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
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 40 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.

affaboutunlabeled
Accelerating AI Innovation in Healthcare Through Mentorship
Divya Kamath, Bemnet Teferi, Rebecca Charow, Jane Mattson, Jessica Jardine, Tharshini Jeyakumar +6 more
2024· article· en· Studies in health technology and informatics· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Proceedings of the 2024 Transplant AI Symposium
Sara Naimimohasses, Shaf Keshavjee, Bo Wang, Mike Brudno, Aman Sidhu, Mamatha Bhat
2024· article· en· Frontiers in Transplantation· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Can Artificial Intelligence Diagnose Knee Osteoarthritis?
Mihir Tandon, Nitin Chetla, Adarsh Mallepally, Botan Zebari, Sai Samayamanthula, Jonathan Silva +4 more
2025· article· en· JMIR Biomedical Engineering· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affvenueno abstractunlabeled
Medicine's digital revolution
Antonio Yaghy
2024· article· en· Canadian Medical Education Journal· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Legal Risks of Adversarial Machine Learning Research
Ram Shankar Siva Kumar, Jonathon W. Penney, Bruce Schneier, Kendra Albert
2020· preprint· en· arXiv (Cornell University)· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
MEDICARE AI-ASSISTED AI IN HEALTHCARE
Nitish Nagar, Plaban Roy, Saurabh Deswal, Parth H Pandya, Aashna Bhardwaj, Parisa Naraei
2021· article· en· GLOBAL JOURNAL FOR RESEARCH ANALYSIS· Medicine
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About