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
Pharmacology and Obesity Treatment
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.

592 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.
592 works in the cohort · of 4,299,418page 11 of 12

Labels cover 3 of 592 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 592 of 592 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.

affvenueaboutunlabeled
Anti-Obesity Medications: An Update for Canadian Physicians
Renuca Modi, Rameez Kabani, Jerry T. Dang, Sarah Chapelsky, Arya M. Sharma
2020· article· en· Canadian Journal of General Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Addressing obesity
Alison Coutts
2009· article· en· Gastrointestinal Nursing· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Benzoxaborole
Dennis G. Hall
2024· other· en· Encyclopedia of Reagents for Organic Synthesis· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
<i>Journal of Diabetes</i> NEWS
Benjamin M. Kozak, Hannah C. Deming, Melissa Y. Tjota, Kelly L. Close
2012· article· fr· Journal of Diabetes· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Erratum
2006· erratum· en· The Canadian Journal of Psychiatry· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Foreword
P. Boisvert, Daniel Richard
2003· article· en· International Journal of Obesity· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Acknowledgement to Reviewers
2025· article· en· Obesity Facts· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affvenueno abstractunlabeled
Therapeutic Relationship in Obesity Treatment
Stephen Stotland, Caroline Laroque
2013· article· en· Canadian Journal of Diabetes· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Understanding Obesity Management in CKD Patients
Michael Chiu, Kathy Koyle, Arsh Jain
2021· article· en· Journal of the American Society of Nephrology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About