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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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Health Policy Implementation Science
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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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aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

4,272 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.
4,272 works in the cohort · of 4,299,418page 60 of 86

Labels cover 308 of 4,272 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 4,272 of 4,272 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
Engagement and partnership with consumers and communities in the co-design and conduct of Research: Lessons from the INtravenous iron polymaltose for First Nations Australian patients with high FERRitin levels on haemodialysis (INFERR) clinical trial
Stephanie Long, Cheryl Ross, J Koops, Katherine Coulthard, Jane Nelson, Archana Khadka Shapkota +27 more
2024· letter· en· Research Involvement and Engagement· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
1
citations
aboutno affunlabeled
Down the Drain
2008· article· en· Open Collections· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
Expediting research to practice for maximum impact
Nora Spinks, Sanela Dursun, Jitender Sareen, Lacey Cranston, Nathan Battams
2020· article· en· Journal of Military Veteran and Family Health· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
0
citations
affno abstractunlabeled
The nurse, the framework, and the digital future
Nicola Straiton, Sandra Lauck, Krystina B. Lewis
2024· article· en· European Journal of Cardiovascular Nursing· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About