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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.

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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 125 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.

affvenueaboutunlabeled
Une intégration novatrice pour aller de l’avant
Giovanna Sirianni, Betty Onyura, Sarah Kawaguchi, Amy Freedman, Batya Grundland, Elliot Lass +3 more
2022· article· fr· Canadian Family Physician· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Modelling the Learning Curves of Incoming Surgical Trainees
Marisa Louridas, Teodor Grantcharov, Neil Seeman, A Iancu, D. Steele, Najma Ahmed +1 more
2017· article· en· Journal of Minimally Invasive Gynecology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
The Expert When the Only
Chantal Phillips
2020· article· en· New England Journal of Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Medical Education in Internal Medicine
Daniel Brandt Vegas, Leslie Martin, Irene Ma, Philip K F Hui, Ford Bursey
2021· article· en· Canadian Journal of General Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
MEDICAL EDUCATION IS CHANGING IN PAKISTAN
İftikhar Ahmad
2021· article· en· Gomal Journal of Medical Sciences· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueunlabeled
Remaniement dû à la pandémie
Sharon Domb, Eden Manly, Debbie Elman
2021· article· fr· Canadian Family Physician· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Medical Expertise
Maya J. Goldenberg
2025· book-chapter· en· Oxford University Press eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Five years of competency-based medical education in Canadian urology
David‐Dan Nguyen, Marie-Lyssa Lafontaine, Uday Mann, Nicolas Siron, Julien Letendre, Mélanie Aubé-Peterkin +4 more
2024· article· en· Canadian Urological Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Building on Past Experience
J. Donald Boudreau, Eric J. Cassell, Abraham Fuks
2018· book· en· Oxford University Press eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Optimizing Our Skills Labs to Improve Quality of Learning
Claude Mailhot, Francis Richard, Laurence Juteau, Ema Ferreira, Gilles Leclerc, Cadieux Marie-Josée +1 more
2023· article· en· American Journal of Pharmaceutical Education· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About