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
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 322 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.

affunlabeled
Feedback in family medicine training.
Jaspreet Mangat, Emy Martineau-Rheault, Kyle MacDonald, Jemy Joseph
2016· article· en· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Choosing our narrative wisely.
Peter Kuling
2020· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Pemphigus foliaceous.
Elizabeth C. Goodale
2019· article· en· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Treating morning sickness PRN?
Gideon Koren
2013· article· en· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Hepatitis C: mental health issues.
William Rowe, Jocelyn Rowe, Leah Malowaniec
2000· article· en· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Nasogastric tube syndrome.
Julie Brousseau, Karen Kost
2007· article· en· PubMed· Nursing
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
[no title]
Christina Korownyk, James McCormack, Michael R. Kolber, Scott Garrison, G. Michael Allan
2017· article· fr· PubMed· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
aboutno affunlabeled
AMA: set standards, not user fees.
R Cairney
2001· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
The occasional wound "gluing".
Gordon Brock
2016· article· fr· PubMed· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
Blood donor ban upheld.
Alison Symington
2011· article· en· PubMed· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
The staff and the "fiery serpent".
Mary Coffman Crocker
2002· letter· en· PubMed· Social Sciences
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About