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
Surgical Simulation and Training
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.

2,557 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.
2,557 works in the cohort · of 4,299,418page 41 of 52

Labels cover 3 of 2,557 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 2,557 of 2,557 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.

affno abstractunlabeled
Designing the Endoscopy Lab to Optimize Training
Malorie Simons, Chandni Pattni, Samir C. Grover, Tyler M. Berzin
2023· article· en· Clinical Gastroenterology and Hepatology· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
afffundvenueunlabeled
Visualization Performance Through Simulation Based Learning
Thomas E. Doyle, David Musson, Jon-Michael J. Booth
2012· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Monitoring electromagnetic tracking error using redundant sensors
Vinyas Harish, Eden Bibic, András Lassó, Matthew Holden, Thomas Vaughan, Zachary M. C. Baum +2 more
2017· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Simulation in Urology
Hamid Abboudi, Muhammad Shamim Khan, Prokar Dasgupta, Kamran Ahmed
2019· other· en· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Common surgical training program: standardization of learning quality
L Álvarez Martínez, Eduardo Ruiz Aja, MP Valdivieso Castro, TM Cardenal Alonso-Allende, CM Gálvez Estévez, A Galbarriatu Gutiérrez +2 more
2022· article· en· Cirugía pediátrica· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
venueaboutno affunlabeled
Corrections
Daryl S Kucey, Andrew Hill, Thomas Lindsay
2006· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About