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
Substance Abuse Treatment and Outcomes
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.

3,732 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.
3,732 works in the cohort · of 4,299,418page 38 of 75

Labels cover 16 of 3,732 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 3,732 of 3,732 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.

affaboutunlabeled
A Complex Picture
Norman Giesbrecht, Gerald Thomas
2010· article· en· Nordic Studies on Alcohol and Drugs· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Substance-Induced Mood Disorders: A Scoping Review
Ashley E. Kivlichan, Angela Praecht, Cindy Wang, Tony P. George
2024· review· en· Current Addiction Reports· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Research and the alcohol industry
Gerhard Gmel, Jean‐Luc Heeb, Jürgen Rehm
2003· letter· en· Addiction· Medicine
machine prediction:candidate · stsconsensus · none
7
citations
affunlabeled
At-risk drinking in employed men and women
Carlos A. Mazas, Ludmila Cofta‐Woerpel, Patricia Daza, Rachel T. Fouladi, Jennifer Irvin Vidrine, Paul M. Cinciripini +2 more
2006· article· en· Annals of Behavioral Medicine· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Evidence‐Based Drug Policies
Serge Brochu
2006· article· en· Journal of Scandinavian Studies in Criminology and Crime Prevention· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
Assessing the impacts of alcohol policies
Michele Cecchini, Marion Devaux, Franco Sassi
2015· paratext· en· OECD health working papers· Medicine
machine prediction:candidate · noneconsensus · none
7
citations

How this was built: Screen · Findings · About