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
International Wound Journal
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.

259 results · 1 filter active ·
Results by year
20042025
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.
259 works in the cohort · of 4,299,418page 3 of 6

Labels cover 2 of 259 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 259 of 259 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Patient perception of wound photography
Sheila C. Wang, John A. E. Anderson, Duncan VB Jones, Robyn Evans
2014· article· en· International Wound Journal· Medicine
distilled prediction:candidate · noneconsensus · none
19
citations
affunlabeled
ISTAP classification for skin tears: Validation for Brazilian Portuguese
Cinthia Viana Bandeira da Silva, Ticiane Carolina Gonçalves Faustino Campanili, Noélle de Oliveira Freitas, Kimberly LeBlanc, Sharon Baranoski, Vera Lúcia Conceição de Gouveia Santos
2019· article· en· International Wound Journal· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · none
13
citations
aboutno affunlabeled
Five years and counting
Keith G Harding
2008· article· en· International Wound Journal· Medicine
distilled prediction:candidate · noneconsensus · none
9
citations
← previousnext →

How this was built: Screen · Findings · About