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
Healthcare cost, quality, practices
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.

1,204 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.
1,204 works in the cohort · of 4,299,418page 22 of 25

Labels cover 31 of 1,204 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 1,204 of 1,204 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
Readers panel
Steve Flatt, Carol Singleton, Julie Clarke, Linda Drake
2010· article· en· Nursing Standard· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1097-8690(13)70040-4
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
CKD PATHOPHYSIOLOGY AND CLINICAL STUDIES
Ilona Kurnatowska, P. Grzelak, Anna Masajtis‐Zagajewska, Marta Kaczmarska, Cees Vermeer, Katarzyna Maresz +300 more
2014· article· en· Nephrology Dialysis Transplantation· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
De rol van de arts in het ziekenhuis
Renaat Peleman, Caroline Vogels
2016· article· nl· Ghent University Academic Bibliography (Ghent University)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0029-7437(10)70220-2
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Calling to Safeguard the Future of Classical Hematology
Yves Pastore, Victoria Price, Sara J. Israels, Anthony K.C. Chan, Mark Belletrutti, Aisha Bruce +8 more
2025· letter· en· Pediatric Blood & Cancer· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Why I am Choosing Wisely
Anthony Train
2019· article· en· Canadian Family Physician· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
The Evolving Practice of Preventative Medicine
Michelle Lai, Julia Pon
2012· article· en· UBC Faculty of Medicine medical journal· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1634-6939(21)89692-1
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueaboutno affunlabeled
Correction
2025· erratum· en· Canadian Family Physician· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Klug entscheiden
Peter R. Galle, Götz Geldner, Julia Hecht, Alfred Königsrainer, Frank-Gerald Pajonk, Julia Rojahn
2016· article· de· Lege artis - Das Magazin zur ärztlichen Weiterbildung· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Heste og mennesker deler hjertelidelser
Mette Krogsgaard, Rikke Buhl
2015· article· da· Research at the University of Copenhagen (University of Copenhagen)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affgemma · no categorygpt · no categorymodels split
Evaluating De‐Implementation Interventions
Beatriz Goulão, Eva W. Verkerk, Kednapa Thavorn, Justin Presseau, Monica Taljaard
2023· other· en· Health Professions
machine prediction:candidate · metaresearchconsensus · none
0
citations
affvenueaboutunlabeled
Les meilleures études en 2024 adaptées aux soins primaires
Samantha S Moe, Betsy Thomas, Danielle Perry, Émélie Braschi, Nicolas Dugré, Jamie Falk +3 more
2025· review· en· Canadian Family Physician· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About