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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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Primary Care and Health Outcomes
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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.

4,685 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.
4,685 works in the cohort · of 4,299,418page 22 of 94

Labels cover 32 of 4,685 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 4,685 of 4,685 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.

affaboutunlabeled
Comprehensive care and education.
Allyn Walsh, Jill Konkin, David Tannenbaum, Jonathan Kerr, Andrew J. Organek, Ean Parsons +3 more
2011· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
aboutno affunlabeled
International Observer
Arch G. Mainous, Richard Baker, Azeem Majeed, Richelle J. Koopman, Charles J. Everett, Sonia Saxena +1 more
2006· article· en· Public Health Reports· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
affvenueaboutunlabeled
Using Archetypes to Design Services for High Users of Healthcare
Samuel Vaillancourt, Ilan Shahin, Payal Aggarwal, Steve Pomedli, Leigh Hayden, Laura Pus +1 more
2014· letter· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
venueaboutno affunlabeled
Integrating pharmacists into family practice teams
Kevin Pottie, Barbara Farrell, Susan Haydt, Lisa Dolovich, Connie Sellors, Natalie Kennie‐Kaulbach +2 more
2008· article· en· Canadian Family Physician· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
aboutno affunlabeled
Estimating the prevalence of depression from EMRs.
Joseph H. Puyat, Wilson W. Marhin, Duncan J. Etches, R. McL Wilson, Ruth Elwood Martin, Kuljit Kaur Sajjan +1 more
2013· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
aboutno affno abstractunlabeled
Canada's time to act
Jocalyn Clark, Richard Horton
2018· article· en· The Lancet· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations

How this was built: Screen · Findings · About