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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
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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 53 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
Two Cheers for Milestones
Louis N. Pangaro
2015· editorial· en· Journal of Graduate Medical Education· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Engaged at the Extremes
Kathryn Myers, Elaine Zibrowski, Lorelei Lingard
2012· article· en· Academic Medicine· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affaboutunlabeled
<i>Sanokondu</i>
Jamiu O. Busari, Ming‐Ka Chan, Deepak Dath, Anne Matlow, Diane de Camps Meschino
2018· article· en· Leadership in health services· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affaboutunlabeled
Measuring Physicians’ Productivity
Guido Filler, Vanessa Burkoski, Gary Tithecott
2013· article· en· Academic Medicine· Medicine
machine prediction:candidate · metaresearchconsensus · none
8
citations
affvenueunlabeled
Seeing medicine’s hidden curriculum
Renata Leong, Kennedy Ayoo
2019· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
afffundno abstractunlabeled
Creation and Dissemination of a Multispecialty Graduate Medical Education Curriculum in Pediatric and Adolescent Gynecology: The North American Society for Pediatric and Adolescent Gynecology Resident Education Committee Experiences
Carol Wheeler, Karen-Jill Browner-Elhanan, Yolanda N. Evans, Nathalie Fleming, Patricia Huguelet, Nicole W. Karjane +3 more
2017· article· en· Journal of Pediatric and Adolescent Gynecology· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
venueno affunlabeled
Practical Tips for Setting Up and Running OSCEs
Emily Hall, Sarah Baillie, Julie Hunt, Alison Catterall, Lissann Wolfe, Annelies Decloedt +2 more
2022· article· en· Journal of Veterinary Medical Education· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Milestones: Quo Vadis?
Felix Ankel, Doug Franzen, Jason R. Frank
2013· letter· en· Academic Emergency Medicine· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
aboutno affunlabeled
Case-based discussion
Nick Brown, Gareth Holsgrove, Sadira Teeluckdharry
2011· article· en· Advances in Psychiatric Treatment· Medicine
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About