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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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Machine Learning in Healthcare
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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.

1,008 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.
1,008 works in the cohort · of 4,299,418page 1 of 21

Labels cover 4 of 1,008 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 1,008 of 1,008 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.

afffundno abstractunlabeled
Artificial intelligence in medicine
Pavel Hamet, Johanne Tremblay
2017· review· en· Metabolism· Computer Science
machine prediction:candidate · noneconsensus · none
2,264
citations
affunlabeled
From Big Data to Precision Medicine
Tim Hulsen, Saumya Shekhar Jamuar, Alan R. Moody, Jason H. Karnes, Orsolya Varga, Stine Hedensted +3 more
2019· review· en· Frontiers in Medicine· Computer Science
machine prediction:candidate · noneconsensus · none
464
citations
afffundunlabeled
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin, T. Brian, Gregory P. Way +30 more
2017· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Computer Science
machine prediction:candidate · noneconsensus · none
309
citations
fundno affno abstractunlabeled
Machine learning in clinical decision making
Lorenz Adlung, Yotam Cohen, Uria Mor, Eran Elinav
2021· review· no· Med· Computer Science
machine prediction:candidate · noneconsensus · none
273
citations
afffundno abstractunlabeled
Brief History of Artificial Intelligence
Nikesh Muthukrishnan, Farhad Maleki, Katie Ovens, Caroline Reinhold, Behzad Forghani, Reza Forghani
2020· review· en· Neuroimaging Clinics of North America· Computer Science
machine prediction:candidate · noneconsensus · none
217
citations
afffundunlabeled
Supervised machine learning tools: a tutorial for clinicians
Lucas Lo Vercio, Kimberly Amador, Jordan J. Bannister, Sebastian Crites, Alejandro Gutierrez, M. Ethan MacDonald +10 more
2020· review· en· Journal of Neural Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
164
citations
fundno affunlabeled
Big data and medical research in China
Luxia Zhang, Haibo Wang, Quanzheng Li, Ming‐Hui Zhao, Qimin Zhan
2018· article· en· BMJ· Computer Science
machine prediction:candidate · metaresearchconsensus · none
153
citations
fundvenueno affunlabeled
Implementing machine learning in medicine
Amol A. Verma, Joshua Murray, Russell Greiner, Joseph Cohen, Kaveh G Shojania, Marzyeh Ghassemi +3 more
2021· letter· en· Canadian Medical Association Journal· Computer Science
machine prediction:candidate · noneconsensus · none
136
citations

How this was built: Screen · Findings · About