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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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Artificial Intelligence in Healthcare
Retraction
Abstract
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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
fundfunder
venuejournal
aboutaboutness

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

843 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.
843 works in the cohort · of 4,299,418page 7 of 17

Labels cover 7 of 843 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 843 of 843 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
A 12 Week Full Stack Web Course in 2017
Arnold Rosenbloom, Larry Yueli Zhang
2017· article· en· Health Professions
distilled prediction:candidate · sts+insufficient_payloadconsensus · insufficient_payload
3
citations
affvenueaboutunlabeled
Big Data: Why Should Canadian Nurse Leaders Care?
Sally Remus, Lorie Donelle
2019· article· en· Nursing leadership· Health Professions
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
3
citations
fundno affno abstractunlabeled
International Conference on Biomedical and Health Informatics 2024
2025· book· en· World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany· Health Professions
distilled prediction:candidate · metaepi_narrow+research_integrity+insufficient_payloadconsensus · research_integrity
3
citations
affno abstractunlabeled
Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180]
Edward De Brouwer, Thijs Becker, Yves Moreau, Eva Havrdová, María Trojano, Sara Eichau +35 more
2021· erratum· en· Computer Methods and Programs in Biomedicine· Health Professions
distilled prediction:candidate · metaepi_narrow+research_integrityconsensus · research_integrity
3
citations
venueno affunlabeled
Heart Disease: Application of the K-Nearest Neighbor (KNN) Method
Diah Puspitasari, Alief Juan Aprian, Erma Delima Sikumbang, Kresna Ramanda, Sulaeman Hadi Sukmana, Qudsiah Nur Azizah
2024· article· fr· Ingénierie des systèmes d information· Health Professions
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
2
citations
affunlabeled
Graphics to Display the Data Distribution
Clemens Reimann, R G Garrett, Rudolf Dutter
2008· other· en· Health Professions
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
2
citations
affno abstractunlabeled
Introduction to the Minitrack on Big Data on Healthcare Application
Kelvin Tsoi, Simon Poon, Patrick C. K. Hung
2018· article· en· Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences· Health Professions
distilled prediction:candidate · metaepi_narrow+sts+open_scienceconsensus · none
2
citations
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How this was built: Screen · Findings · About