MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Clinical Reasoning and Diagnostic Skills
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.

1,287 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.
1,287 works in the cohort · of 4,299,418page 19 of 26

Labels cover 7 of 1,287 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 1,287 of 1,287 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
Why?
Geoff Norman
2017· editorial· en· Advances in Health Sciences Education· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
In Reply to Eichbaum
Geoffrey R. Norman, Henk G. Schmidt, Jonathan S. Ilgen
2019· letter· en· Academic Medicine· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
In Reply to Croskerry and to Patel and Bergl
Geoffrey R. Norman, Jonathan Sherbino, Jonathan S. Ilgen, Sandra Monteiro
2017· letter· en· Academic Medicine· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
afffundno abstractunlabeled
Finding Out Your Doctor Made a Mistake
Allan S. Detsky
2020· letter· en· Journal of General Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affvenueno abstractunlabeled
Raisonnement clinique
Togas Tulandi, Paul J. Yong
2020· editorial· fr· Journal of Obstetrics and Gynaecology Canada· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Language games and scholarly writing
Rachel Ellaway
2021· editorial· en· Advances in Health Sciences Education· Medicine
machine prediction:candidate · scholarly_communicationconsensus · none
1
citations
affno abstractunlabeled
The Physician’s Bias
Farrah J. Mateen
2005· article· en· American Journal of Psychiatry· Medicine
machine prediction:candidate · metaresearchconsensus · none
1
citations
affunlabeled
Introducing “Viewpoint: Turning the Air Blue”
Fernando J. Martínez, Andrew Bush, Laurent Brochard, MeiLan K. Han, Sanjay H. Chotirmall
2023· letter· en· American Journal of Respiratory and Critical Care Medicine· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
L’ignorance et l’opinion, un couple heureux
Serge Larivée, Carole Sénéchal
2019· article· fr· Les cahiers internationaux de psychologie sociale· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
The Unexplored Value of “Normal”
Andrew B. LoGiudice, Matthew Sibbald, Sandra Monteiro
2021· article· en· Archives of Medicine and Health Sciences· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Practicing With Intent: How to Teach an Old Dogma New Tricks
Matthew C. Phillips, Kusha Davar, Sarah Freling, Steven Y. C. Tong, Todd C. Lee, Emily G. McDonald +10 more
2025· article· en· Clinical Infectious Diseases· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Pro forma: impact on communication skills?
Marie Morris, Gary Donohoe, Martina Hennessy, Caoilte Ó Ciardha
2013· article· en· The Clinical Teacher· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
← previousnext →

How this was built: Screen · Findings · About