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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 12 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.

affno abstractunlabeled
Assessing Tutorial-Based Assessment
Kevin W. Eva
2001· article· en· Advances in Health Sciences Education· Medicine
machine prediction:candidate · metaresearchconsensus · none
60
citations
aboutno affunlabeled
Deconstructing Quality in Education Research
Gail M. Sullivan
2011· article· en· Journal of Graduate Medical Education· Medicine
machine prediction:candidate · metaresearchconsensus · metaresearch
60
citations
affaboutunlabeled
Addressing the health advocate role in medical education
Suzanne Boroumand, Michael J. Stein, Mohammad Jay, Julia W. Shen, Michael Hirsh, Shafik Dharamsi
2020· article· en· BMC Medical Education· Medicine
machine prediction:candidate · noneconsensus · none
59
citations
affunlabeled
Twelve tips for curriculum renewal
Peter J. McLeod, Yvonne Steinert
2014· article· en· Medical Teacher· Medicine
machine prediction:candidate · noneconsensus · none
59
citations
affunlabeled
The curious case of case study research
Jennifer Cleland, Anna MacLeod, Rachel Ellaway
2021· article· en· Medical Education· Medicine
machine prediction:candidate · metaresearchconsensus · metaresearch
59
citations
affunlabeled
Competency-Based Medical Education for Plastic Surgery
Aaron Knox, Mirko S. Gilardino, Steve J. Kasten, Richard J. Warren, Dimitri J. Anastakis
2014· article· en· Plastic & Reconstructive Surgery· Medicine
machine prediction:candidate · noneconsensus · none
58
citations
affunlabeled
Making the Case for History in Medical Education: Fig. 1.
David S. Jones, Jeremy A. Greene, Jacalyn Duffin, John Harley Warner
2014· article· en· Journal of the History of Medicine and Allied Sciences· Medicine
machine prediction:candidate · noneconsensus · none
58
citations

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