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
Statistical Methods in Medical Research
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.

233 results · 1 filter active ·
Results by year
20012025
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.
233 works in the cohort · of 4,299,418page 1 of 5

Labels cover 2 of 233 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 233 of 233 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.

afffundaboutunlabeled
Propensity score matching and complex surveys
Peter C. Austin, Nathaniel Jembere, Maria Chiu
2016· article· en· Statistical Methods in Medical Research· Mathematics
machine prediction:candidate · noneconsensus · none
192
citations
aboutno affunlabeled
Analysis of data with excess zeros
Peter A. Lachenbruch
2002· article· en· Statistical Methods in Medical Research· Mathematics
machine prediction:candidate · metaresearchconsensus · none
156
citations
afffundunlabeled
Analysis of repeated events
Richard J. Cook, Jerald F. Lawless
2002· review· en· Statistical Methods in Medical Research· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
143
citations
affunlabeled
Fitting competing risks with an assumed copula
Gabriel Escarela, Jacques F. Carriére
2003· article· en· Statistical Methods in Medical Research· Mathematics
machine prediction:candidate · noneconsensus · none
119
citations
affunlabeled
Detecting activation in fMRI data
Keith J. Worsley
2003· article· en· Statistical Methods in Medical Research· Neuroscience
machine prediction:candidate · noneconsensus · none
96
citations
affunlabeled
Prediction intervals with random forests
Marie-Hélène Roy, Denis Larocque
2019· article· en· Statistical Methods in Medical Research· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affunlabeled
Survival forests for data with dependent censoring
Hoora Moradian, Denis Larocque, François Bellavance
2017· article· en· Statistical Methods in Medical Research· Mathematics
machine prediction:candidate · noneconsensus · none
48
citations
affunlabeled
Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend analysis
Laurent Remontet, Zoé Uhry, Nadine Bossard, Jean Iwaz, Aurélien Belot, Coraline Danieli +2 more
2018· article· en· Statistical Methods in Medical Research· Social Sciences
machine prediction:candidate · noneconsensus · none
40
citations

How this was built: Screen · Findings · About