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
Journal of the Royal Society of 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.

119 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.
119 works in the cohort · of 4,299,418page 3 of 3

Labels cover 1 of 119 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 119 of 119 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
The wrong message?
Steven B. Karch
2000· editorial· en· Journal of the Royal Society of Medicine· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
COVID-19: still much to learn
Joel Lexchin
2020· letter· en· Journal of the Royal Society of Medicine· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
<i>NHS Titanic</i> ?
Zbys Fedorowicz
2010· article· he· Journal of the Royal Society of Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Connections with death
P I Reed
2007· letter· en· Journal of the Royal Society of Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Author's Reply
Jawahar Kalra
2005· article· en· Journal of the Royal Society of Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Magnets
Thomas K. Day
2005· letter· en· Journal of the Royal Society of Medicine· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Brain Imaging in Fatigue Syndromes
Eric Berger
2005· letter· en· Journal of the Royal Society of Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Authors’ Reply
Harry A Lee, R. Gabriel, J Philip G Bolton, Amanda J Bale, Mark Jackson
2003· article· en· Journal of the Royal Society of Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Open access: the evidence and the verdict
Stevan Harnad
2006· letter· en· Journal of the Royal Society of Medicine· Decision Sciences
machine prediction:candidate · open_scienceconsensus · none
0
citations
affno abstractunlabeled
Article
Andrew Watson
2005· letter· en· Journal of the Royal Society of Medicine· Materials Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Open access: The evidence and the verdict
Stevan Harnad
2006· article· en· Journal of the Royal Society of Medicine· Decision Sciences
machine prediction:candidate · metaresearch+bibliometrics+open_scienceconsensus · none
0
citations
← previous

How this was built: Screen · Findings · About