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
Health, Medicine and Society
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.

3,958 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.
3,958 works in the cohort · of 4,299,418page 28 of 80

Labels cover 10 of 3,958 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 3,958 of 3,958 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.

venueno affno abstractunlabeled
Fourrer la mort
Mathieu Arsenault
2013· article· fr· Liberté· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0000-0000(09)54093-4
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
Mot de la rédactrice en chef
Sylvie St‐Onge
2009· article· fr· Gestion· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Temps et rythmes en périnatalité
Anne Lacassagne
2022· book-chapter· fr· Érès eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Droit et santé mentale : un tournant ?
Gérard Rossinelli
2005· article· fr· L information psychiatrique· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
National News / Nouvelles nationales
2008· article· en· The Forestry Chronicle· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Filmer les soins
David Pichonnaz, Camille Bécherraz, Isabelle Knutti, Liliana Staffoni, Veronika Schoeb
2017· article· fr· Recherches qualitatives· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Lieu de rencontre
Nicholas Pimlott
2009· article· fr· Canadian Family Physician· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
In memoriam, Marcel Lesieur (1945-2022)
Ugo Piomelli, Olivier Métais
2022· article· fr· Journal of Turbulence· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Rick Neuls MD CCFP
Shane Neilson
2011· article· en· Canadian Family Physician· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About