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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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Medical Teacher
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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.

727 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.
727 works in the cohort · of 4,299,418page 4 of 15

Labels cover 2 of 727 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 727 of 727 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.

affunlabeled
A comprehensive checklist for reporting the use of OSCEs
Madalena Patrício, Miguel Julião, Filipa Fareleira, Meredith Young, Geoffrey R. Norman, António Vaz Carneiro
2009· review· en· Medical Teacher· Medicine
machine prediction:candidate · metaresearchconsensus · metaresearch
62
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
Meaningful feedback through a sociocultural lens
Subha Ramani, Karen D. Könings, Shiphra Ginsburg, Cees van der Vleuten
2019· article· en· Medical Teacher· Social Sciences
machine prediction:candidate · noneconsensus · none
52
citations
affunlabeled
Medical education in Brazil
Renato Antunes dos Santos, Maria do Patrocínio Tenório Nunes
2019· article· en· Medical Teacher· Medicine
machine prediction:candidate · noneconsensus · none
49
citations

How this was built: Screen · Findings · About