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 and Health Practices
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.

1,836 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.
1,836 works in the cohort · of 4,299,418page 34 of 37

Labels cover 11 of 1,836 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 1,836 of 1,836 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.

afffundunlabeled
The enigma of weight: Figures, flux, and fitting in
Katherine Wong, Maxine Myre, Nancy J. Moules, Danielle Lefebvre, Janelle M. Morhun, Jessica F. Saunders +2 more
2022· article· en· Frontiers in Psychology· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Obesity solutions?
Dennis Edell
2015· letter· en· Canadian Medical Association Journal· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affgemma · insufficient_payloadgpt · insufficient_payloadmodels agree
SEE PROFILE
2016· article· en· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Aykroyd, Wallace Ruddell
Carpenter Kj
2001· other· en· Encyclopedia of Life Sciences· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
Family Medicine's Strategies for Geriatric Obesity and Its Complications
Ali Abdullah Alasmari, Mohammed Sami Ghamri, Mortada Muneer Albaqshi Albaqshi, Nouriyah Haider Arishi, Feras Mazin Saleh, Abrar Abdullah Hanafi +4 more
2023· article· en· Journal of Survey in Fisheries Sciences· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
10 of the Most Asked Questions and Challenges in Obesity
Heidi Dutton, Sean Wharton, Emily Kearsley-Ho, Andrea Millard, Sandy Van
2023· article· en· Canadian Journal of General Internal Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About