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 59 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.

affunlabeled
Provider Reactions to Opioid-Prescribing Report Cards
Musheng Alishahi, Katie Olson, Ashley Brooks‐Russell, Jason Hoppe, Carol W. Runyan
2021· article· en· Journal of Public Health Management and Practice· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affvenueno abstractunlabeled
Owning the opioid crisis
Melanie Jaeger, D. Robert Siemens
2019· editorial· en· Canadian Urological Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
fundaboutno affunlabeled
Law enforcement and drug-related crime.
Mary O'Brien
2001· book-chapter· en· Health Research Board eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/j.ccol.2020.07.031
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affaboutunlabeled
Editorial Comment
Joana Dos Santos, Mandy Rickard, Armando J. Lorenzo
2024· editorial· es· The Journal of Urology· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutunlabeled
Predictors of Perioperative Opioid Use in Hysterectomy Patients
Azra Shivji, Samantha Benlolo, John G. Hanlon, Lindsay Shirreff, H Husslein, Eliane M. Shore
2025· article· en· JSLS Journal of the Society of Laparoscopic & Robotic Surgeons· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Forse toename voorgeschreven opioïden in Nederland
Jan van Amsterdam, H. H. C. Wartenberg, Wim van den Brink
2015· article· nl· Data Archiving and Networked Services (DANS)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Opioids in non-cancer pain
D. Choquette, Triona McCarthy, Janaina Silva da Costa Rodrigues, Alexander Kelly, Farah A. Husein-Bhabha
2004· article· en· Journal of Pain· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About