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
Obesity, Physical Activity, Diet
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.

5,973 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.
5,973 works in the cohort · of 4,299,418page 113 of 120

Labels cover 15 of 5,973 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 5,973 of 5,973 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
Baseline Fitness Measures In A Cohort Of Obese Children
James E. Potts, Astrid M. De Souza, Kristin Houghton, Shubhayan Sanatani, W. Jack Duncan, G Sándor
2008· article· en· Medicine & Science in Sports & Exercise· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
P58
Julie Garden-Robinson, Rita Ussatis, Kim Lipetzky
2007· article· en· Journal of Nutrition Education and Behavior· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
Serum Biomarkers Validate a Pictorial Vegetable Behavior Tool
Marilyn S. Townsend, Mical K. Shilts, Lee‐Ann H. Allen, Dennis M. Styne, L. R. Woodhouse
2015· article· en· Journal of Nutrition Education and Behavior· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/j.yped.2015.06.033
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s1558-0164(10)70353-9
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
P72
Mical K. Shilts, Marcel Horowitz, Anna Martin, Cathi Lamp, D G Smith, Lenna Ontai +1 more
2006· article· en· Journal of Nutrition Education and Behavior· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Lasius Fabricius
2019· article· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Letters to the Editor
Josh Golin
2006· letter· en· Journal of Nutrition Education and Behavior· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About