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 27 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.

affunlabeled
Variational Fair Clustering
Imtiaz Masud Ziko, Jing Yuan, Éric Granger
2021· article· en· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
26
citations
afffundno abstractunlabeled
Evaluating Canadians’ Values for Drug Coverage Decision Making
Shirin Rizzardo, Nick Bansback, Nick Dragojlovic, Conor M.W. Douglas, Kathy H. Li, Craig Mitton +3 more
2018· article· en· Value in Health· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Registry-based trials: a potential model for cost savings?
Brett R. Anderson, Evelyn Gotlieb, Kevin D. Hill, Kimberly E. McHugh, Mark A. Scheurer, Carlos M. Mery +17 more
2020· article· en· Cardiology in the Young· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
26
citations
afffundunlabeled
Spline‐based accelerated failure time model
Menglan Pang, Robert W. Platt, Tibor Schuster, Michał Abrahamowicz
2020· article· en· Statistics in Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
26
citations
afffundunlabeled
Clinical decision analysis in perinatology
Rohan D’Souza, Prakesh S. Shah, Beate Sander
2017· article· en· Acta Obstetricia Et Gynecologica Scandinavica· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Improving ethics analysis in health technology assessment
Katherine Duthie, Kenneth Bond
2011· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
26
citations
affno abstractunlabeled
The End of the Risk–Treatment Paradox?
Finlay A. McAlister
2011· letter· en· Journal of the American College of Cardiology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
25
citations

How this was built: Screen · Findings · About