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 Systems, Economic Evaluations, Quality of Life
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.

6,862 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.
6,862 works in the cohort · of 4,299,418page 72 of 138

Labels cover 97 of 6,862 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 6,862 of 6,862 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.

affno abstractunlabeled
Screening and litigation
J R Benson
2000· article· en· BMJ· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Primes de risque et soins de santé
Christophe Courbage
2009· article· fr· L Actualité économique· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
To Get the Best Outcome, Choose the Best Outcome
Achilleas Thoma, Felmont F. Eaves
2017· letter· en· Aesthetic Surgery Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
Health Technology Assessment Reports for Non-Oncology Medications in Canada from 2018 to 2022: Methodological Critiques on Manufacturers’ Submissions and a Comparison between Manufacturer and Canadian Agency for Drugs and Technologies in Health (CADTH) Analyses
Fatemeh Mirzayeh Fashami, Jean‐Éric Tarride, Behnam Sadeghirad, Kimia Hariri, Amirreza Peyrovinasab, Mitchell Levine
2024· article· en· PharmacoEconomics - Open· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
2
citations
affno abstractunlabeled
EDITORIAL
Koon Teo, Laurel Taylor
2000· editorial· en· Evidence-based Cardiovascular Medicine· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations

How this was built: Screen · Findings · About