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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.

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Innovations in Medical Education
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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.

6,256 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
6,256 works in the cohort · of 4,299,418page 120 of 126

Labels cover 27 of 6,256 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 6,256 of 6,256 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
ESSU Aug 29 2015
2015· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affvenueaboutunlabeled
A new way forward via innovative integration
Giovanna Sirianni, Betty Onyura, Sarah Kawaguchi, Amy Freedman, Batya Grundland, Elliot Lass +3 more
2022· article· en· Canadian Family Physician· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Introduction to the Symposium
Deborah T. Kochevar, Elizabeth A. Stone
2008· article· en· Journal of Veterinary Medical Education· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affvenueno abstractunlabeled
Let’s not forget our residents in training
Armen Aprikian
2023· article· en· Canadian Urological Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Supple 1. Survey questionnaire.pdf
Dalia Karol, Debra Pugh
2020· dataset· fr· Harvard Dataverse· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affvenueunlabeled
Why are doctors so hard to educate?
Edward J. Harvey, Chad G. Ball
2023· editorial· en· Canadian Journal of Surgery· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
International Briefs
2023· article· en· Journal of Medical Regulation· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Co-Editors' notes
Michael Glasser, Danette McKinley, Payal Bansal
2022· editorial· en· Education for Health· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
In Reply to Bohler et al
Shiphra Ginsburg, Lynfa Stroud
2023· letter· en· Academic Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Making a start on the tenure track
Shira Joudan
2023· article· en· Nature Chemistry· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
Revenir fort de connaissances
L. Lee Dupuis
2004· article· fr· The Canadian Journal of Hospital Pharmacy· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About