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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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Studies in health technology and informatics
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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.

990 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.
990 works in the cohort · of 4,299,418page 15 of 20

Labels cover 2 of 990 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 990 of 990 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
AI-Enhanced Speech Recognition in Triage
Ahmed Elhilali, Vanessa Brügger, Isabelle Tschannen, Wolf E. Hautz, Gert Krummrey
2025· article· en· Studies in health technology and informatics· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Do Health Technology Safety Issues Vary by Vendor?
Elizabeth M. Borycki, Amr Farghali, André Kushniruk
2022· article· en· Studies in health technology and informatics· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Mobile ICT Support for the Continuum of Care
Masako Miyazaki, Toshio Ohyanagi, Bonnie Dobbs, Marguerite Rowe, Steve Sutphen, James Miller +1 more
2009· article· en· Studies in health technology and informatics· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
The Mediating Role of Facebook Fan Pages
Wen‐Hai Chih, Li‐Chun Hsu, Kaiyu Wang, Kuan‐Yu Lin
2014· article· en· Studies in health technology and informatics· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
National Strategies for Health Data Interoperability
Mu-Hsing Kuo, André Kushniruk, Elizabeth M. Borycki, Chien‐Yeh Hsu, Chung-Liang Lai
2011· article· en· Studies in health technology and informatics· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About