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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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Journal of Research in Interprofessional Practice and Education
Topic
Retraction
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Study design
Label agreement
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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.

148 results · 1 filter active ·
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20092024
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
148 works in the cohort · of 4,299,418page 2 of 3

Labels cover 0 of 148 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 148 of 148 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.

afffundvenueaboutunlabeled
The Timely Open Communication for Patient Safety Project
Margo Paterson, Jennifer Medves, Nancy Dalgarno, Anne O’Riordan, Robyn Grigg
2013· article· en· Journal of Research in Interprofessional Practice and Education· Health Professions
machine prediction:candidate · noneconsensus · none
9
citations
venueno affunlabeled
Interprofessional Learning through a Digital Platform
Frøydis Vasset, Siri Brynhildsen, Bente Kvilhaugsvik
2019· article· en· Journal of Research in Interprofessional Practice and Education· Health Professions
machine prediction:candidate · noneconsensus · none
9
citations
venueno affunlabeled
An Autoethnographic Study of Interprofessional Education Partnerships
Samantha Hurst, Karen Macauley, Linda Awdishu, Kathleen Sweeney, Sophie S Hutchins, Jennifer Namba +4 more
2018· article· en· Journal of Research in Interprofessional Practice and Education· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About