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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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Electronic Health Records Systems
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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.

2,603 results · 1 filter active ·
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20002025
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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.
2,603 works in the cohort · of 4,299,418page 49 of 53

Labels cover 6 of 2,603 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 2,603 of 2,603 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
Legends of the Shushwap
2010· other· en· Americanae (AECID Library)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Prescriptive Grammar for Clinical Prescribing Workflow
Kalle Kauranen, Arnold Kim, Phillip Osial
2018· article· en· International Journal of Extreme Automation and Connectivity in Healthcare· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutno abstractunlabeled
Ontario Clinical Education Information Systems
Haig Baronikian, Audrey Danaher
2007· article· en· E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
IT
2015· article· en· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Codebook.
2024· article· en· Figshare· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Interview guide.
2024· article· en· Figshare· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Health Information Standards
Stergiani Spyrou, Panagiotis D. Bamidis, Nicos Maglaveras
2011· book-chapter· en· IGI Global eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An ontology for healthcare systems
François Goyer, Paul Fabry, Adrien Barton, Jean‐François Éthier
2022· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
What’s in a drug name?
R. Cheung, Sarah H. Goodwin
2013· article· fr· Canadian Pharmacists Journal / Revue des Pharmaciens du Canada· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Document Security
Charles R. McConnell
2003· article· en· The Health Care Manager· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About