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
Innovations in Medical 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.

6,256 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.
6,256 works in the cohort · of 4,299,418page 79 of 126

Labels cover 27 of 6,256 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 6,256 of 6,256 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
Can Leadership Make a Difference?
Jocelyne Wong, Robert E. Modrow
2004· article· en· Healthcare Management Forum· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Queen’s University Faculty of Health Sciences
Leslie Flynn, Denise Stockley
2020· article· en· Academic Medicine· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
2
citations
aboutno affunlabeled
University of Toronto Faculty of Medicine
Paul Tonin, Stacey Bernstein, M. P. Bryden, Kulamakan Kulasegaram, Marcus Law, Maria Mylopoulos +3 more
2020· article· en· Academic Medicine· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
2
citations
affaboutno abstractunlabeled
Competency-based training: Canadian cardiothoracic surgery
Susan D. Moffatt‐Bruce, Ken Harris, Fraser D. Rubens, Patrick J. Villeneuve, R. Sudhir Sundaresan
2023· editorial· en· Journal of Thoracic and Cardiovascular Surgery· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
McGill University Faculty of Medicine
Joyce S. Pickering, Maryse Grignon
2010· article· en· Academic Medicine· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
afffundvenueaboutunlabeled
Examining Online Health Sciences Graduate Programs in Canada
Paige Colley, Karen Schouten, Nicole Chabot, M Downs, Lauren Anstey, Marc S. Moulin +1 more
2019· article· en· The International Review of Research in Open and Distributed Learning· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Northern Ontario School of Medicine
Marie C. Matte, Joel H. Lanphear, Roger Strasser
2010· article· en· Academic Medicine· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
afffundno abstractunlabeled
Minimal Procedure Numbers Are Not Helpful
Christopher A. Hergott
2020· letter· en· CHEST Journal· Medicine
machine prediction:candidate · metaresearchconsensus · none
2
citations
venueno affunlabeled
Addressing bias in industry-funded CME
Roger Collier
2014· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
2
citations
affunlabeled
Where do physicians start and end?
Rachel Ellaway, David Topps, Maureen Topps
2016· article· en· Medical Education· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
Physician performance feedback in a Canadian academic center
Dennis Garvin, James Worthington, Shaun McGuire, Stephanie Burgetz, Alan J. Forster, Andrea M. Patey +3 more
2017· article· en· Leadership in health services· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Does Targeted Training Improve Residents' Teaching Skills?.
Sean Polreis, Marcel D’Eon, Kalyani Premkumar, Krista Trinder, Deirdre Bonnycastle
2015· article· en· ˜The œjournal of faculty development· Medicine
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About