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
Meta-analysis and systematic reviews
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.

4,076 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.
4,076 works in the cohort · of 4,299,418page 1 of 82

Labels cover 359 of 4,076 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 4,076 of 4,076 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
GRADE guidelines: 3. Rating the quality of evidence
Howard Balshem, Mark Helfand, Holger J. Schünemann, Andrew D Oxman, Regina Kunz, Jan Brożek +4 more
2011· article· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
8,174
citations
affunlabeled
Updated methodological guidance for the conduct of scoping reviews
Micah D.J. Peters, Casey Marnie, Andrea C. Tricco, Danielle Pollock, Zachary Munn, Lyndsay Alexander +3 more
2020· article· en· JBI Evidence Synthesis· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
6,697
citations
afffundunlabeled
A tutorial on pilot studies: the what, why and how
Lehana Thabane, Jinhui Ma, Rong Chu, Ji Cheng, Afisi Ismaila, Lorena P Rios +4 more
2010· article· en· BMC Medical Research Methodology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
3,439
citations
affno abstractunlabeled
Scoping reviews: time for clarity in definition, methods, and reporting
Heather Colquhoun, Danielle Levac, Kelly K. O’Brien, Sharon E. Straus, Andrea C. Tricco, Laure Perrier +2 more
2014· article· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
2,793
citations
afffundno abstractunlabeled
Redefine statistical significance
Daniel J. Benjamin, James O. Berger, Magnus Johannesson, Brian A. Nosek, Eric‐Jan Wagenmakers, Richard A. Berk +65 more
2017· article· en· Nature Human Behaviour· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
2,788
citations
affunlabeled
Summarizing systematic reviews
Edoardo Aromataris, Ritin Fernandez, Christina Godfrey, Cheryl Holly, Hanan Khalil, Patraporn Tungpunkom
2015· article· en· International Journal of Evidence-Based Healthcare· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
2,504
citations

How this was built: Screen · Findings · About