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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 4 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

venueno affunlabeled
“What Is eHealth”: Time for An Update?
Emiel A Boogerd, Tessa Arts, Lucien JLPG Engelen, Tom H van de Belt
2015· article· en· JMIR Research Protocols· Health Professions
distilled prediction:candidate · sts+insufficient_payloadconsensus · insufficient_payload
141
citations
venueno affunlabeled
Mobile Apps in Cardiology: Review
Borja Martínez-Pérez, Isabel de la Torre Díez, Miguel López-Coronado, Jesús Herreros-González
2013· review· en· JMIR mhealth and uhealth· Health Professions
distilled prediction:candidate · metaepi_narrow+sts+research_integrity+insufficient_payloadconsensus · research_integrity
140
citations
affunlabeled
Innovations in e-health
Paul Wicks, Jon Stamford, Martha A. Grootenhuis, Lotte Haverman, Sara Ahmed
2013· article· en· Quality of Life Research· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
131
citations
venueno affunlabeled
A Low-Intensity Mobile Health Intervention With and Without Health Counseling for Persons With Type 2 Diabetes, Part 1: Baseline and Short-Term Results From a Randomized Controlled Trial in the Norwegian Part of RENEWING HEALTH
Astrid Torbjørnsen, Anne Karen Jenum, Milada Cvancarova Småstuen, Eirik Årsand, Heidi Holmen, Astrid Klopstad Wahl +1 more
2014· article· en· JMIR mhealth and uhealth· Health Professions
distilled prediction:candidate · metaepi_narrow+stsconsensus · none
130
citations
venueno affunlabeled
Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis
Md. Mohaimenul Islam, Tahmina Nasrin Poly, Bruno Andres Walther, Yu‐Chuan Li
2020· review· en· JMIR mhealth and uhealth· Health Professions
distilled prediction:candidate · metaepi_narrow+sts+research_integrity+insufficient_payloadconsensus · insufficient_payload
127
citations
fundno affunlabeled
Mobile Phone Tools for Field‐Based Health care Workers in Low‐Income Countries
Brian DeRenzi, Gaetano Borriello, Jonathan Jackson, Vikram Sheel Kumar, Tapan S. Parikh, Pushwaz Virk +1 more
2011· article· en· Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · none
124
citations
venueno affunlabeled
A Path to Better-Quality mHealth Apps
Richard S. Larson
2018· article· en· JMIR mhealth and uhealth· Health Professions
distilled prediction:candidate · metaepi_narrow+sts+insufficient_payloadconsensus · none
123
citations
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