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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.

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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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Mobile Health and mHealth Applications
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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.

4,475 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.
4,475 works in the cohort · of 4,299,418page 58 of 90

Labels cover 50 of 4,475 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 4,475 of 4,475 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.

affunlabeled
Health Apps by Design
Pannel Chindalo, Arsalan Karim, Ronak Brahmbhatt, Nishita Saha, Karim Keshavjee
2017· book-chapter· en· IGI Global eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
Feasibility of Using Short Message Service and In-Depth Interviews to Collect Data on Contraceptive Use Among Young, Unmarried, Sexually Active Men in Moshi, Tanzania, and Addis Ababa, Ethiopia: Mixed Methods Study With a Longitudinal Follow-Up
Francis M. Pima, Martha Oshosen, Kennedy Ngowi, Bruck Messele Habte, Eusebious Maro, Belete Eshete Teffera +7 more
2019· article· en· JMIR Formative Research· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Unveiling the Power of AI Fitness Apps
Zhao Du, Shan Wang, Fang Wang
2025· article· en· Journal of Global Information Management· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Fast food medicine?
Grant Innes
2023· editorial· en· Canadian Journal of Emergency Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Development and Design of E_MOTIV
Guillaume Fontaine, Sylvie Cossette
2022· article· en· CIN Computers Informatics Nursing· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
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How this was built: Screen · Findings · About