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
Clinical and investigative medicine
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.

983 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.
983 works in the cohort · of 4,299,418page 18 of 20

Labels cover 5 of 983 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 983 of 983 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.

venueaboutno affunlabeled
70. Supporting IMG integration into residency trainings
Susan Glover Takahashi, Mitchell Alameddine, Dawn Martin, Sarita Verma, Sarah Edwards
2007· article· en· Clinical and investigative medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
The Infrequency of Spirometry Use in Medical Clinics
Jose Angelo A. De Dios, Richard ZuWallack, Bimalin Lahiri
2007· article· en· Clinical and investigative medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
afffundvenueaboutunlabeled
Cancer genetics—one family at a time
William D. Foulkes
2019· article· en· Clinical and investigative medicine· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
My thanks to our Peer Reviewers
Robert Bortolussi
2019· article· en· Clinical and investigative medicine· Medicine
machine prediction:candidate · metaresearchconsensus · none
0
citations
venueno affunlabeled
Taking the Stress Out of Stress Testing
Rosemary Hohenleitner Miller
2007· article· en· Clinical and investigative medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
CIM: Beyond a new start
Bing Siang Gan
2009· editorial· en· Clinical and investigative medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
INVESTIGATING CRMP4 FUNCTION IN NERVE REGENERATION
Stephan Ong Tone, Yazan Z. Alabed, Adriana Di Polo, Alyson E. Fournier
2008· article· en· Clinical and investigative medicine· Neuroscience
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About