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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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Artificial Intelligence 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.

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 2 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. 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.

venueno affunlabeled
Diagnosing diabetes mellitus using machine learning techniques
Mazen Alzyoud, Raed Alazaidah, Mohammad Aljaidi, Ghassan Samara, Mais Haj Qasem, Muhammad Khalid +1 more
2023· article· en· International Journal of Data and Network Science· Health Professions
machine prediction:candidate · noneconsensus · none
55
citations
afffundunlabeled
Overview of Machine Learning Part 1
Farhad Maleki, Katie Ovens, Keyhan Najafian, Behzad Forghani, Caroline Reinhold, Reza Forghani
2020· review· en· Neuroimaging Clinics of North America· Health Professions
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
A population health perspective on artificial intelligence
Maxime Lavigne, F. E. F. Mussá, Maria I. Creatore, Steven J. Hoffman, David L. Buckeridge
2019· article· en· Healthcare Management Forum· Health Professions
machine prediction:candidate · noneconsensus · none
32
citations
venueno affunlabeled
Analyzing Diabetes Datasets using Data Mining
Saman Hina, Anita Shaikh, Sohail Abul Sattar
2017· article· en· Journal of Basic & Applied Sciences· Health Professions
machine prediction:candidate · noneconsensus · none
29
citations

How this was built: Screen · Findings · About