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
Opioid Use Disorder 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.

4,016 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.
4,016 works in the cohort · of 4,299,418page 62 of 81

Labels cover 19 of 4,016 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 4,016 of 4,016 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.

aboutno affunlabeled
Acknowledgements
2010· article· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Community Based Naloxone Kits
Gill Harvey, Stephanie VandenBerg
2019· article· en· Conference Proceedings of the Academy for Design Innovation Management· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Injectable Opioid Treatment
Eugenia Oviedo‐Joekes
2023· book-chapter· en· Oxford University Press eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
IMF Staff Papers
2008· article· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Application of Data Science to Paramedic Data
J Chris Smith, Wesley S. Burr
2022· dissertation· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
on Substance Abuse
2013· article· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutunlabeled
A Journey Through Tapering
Linda Wilhelm
2023· article· en· Journal of Patient Experience· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Aperçu - Suicides et intoxication aux opioïdes en Alberta (2000-2016)
Elaine Chan, Bruce Mcdonald, Elizabeth Brooks‐Lim, Graham Jones, Kristin Klein, Lawrence W. Svenson
2018· article· fr· Promotion de la santé et prévention des maladies chroniques au Canada· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About