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
Annals of Internal Medicine
Topic
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.

865 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.
865 works in the cohort · of 4,299,418page 9 of 18

Labels cover 1 of 865 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 865 of 865 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
Obesity and Serious Infections
Dimitrios Farmakiotis
2013· letter· en· Annals of Internal Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Hungering for HAART
Julio Montaner, Robert S. Hogg
2007· letter· en· Annals of Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Influenza
2009· review· en· Annals of Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
A Hemorrhage of Off-Label Use
Keyvan Karkouti, Jerrold H. Levy
2011· letter· en· Annals of Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Love
Jean‐Noël Vergnes
2014· article· en· Annals of Internal Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
Metabolically Healthy Overweight and Obesity
Caroline K. Kramer, Bernard Zinman, Ravi Retnakaran
2014· letter· en· Annals of Internal Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
Chlorthalidone Versus Hydrochlorothiazide
Irfan A. Dhalla, Muhammad Mamdani, David N. Juurlink
2013· letter· en· Annals of Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affaboutunlabeled
Risk-Targeted Lung Cancer Screening
Sonya Cressman, Kevin ten Haaf, Stephen Lam, Martin C. Tammemägi
2018· letter· en· Annals of Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Oropouche Virus: A Rising Threat in the Western Hemisphere
Kevin O’Laughlin, Ralph Huits, Michael Libman, Phyllis E. Kozarsky, Davidson H. Hamer
2024· editorial· en· Annals of Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About