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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 15 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.

affno abstractunlabeled
Predicting Patient Length of Stay Using Machine Learning Models
Omar Khaled, Tarek Mostafa, Shaimaa Salah, Mohamed Ezz, Mohamed Omar, Ahmed F. Ali
2024· book-chapter· en· Lecture notes on data engineering and communications technologies· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
DACOS - Dataset
Himesh Nandani, Mootez Saad, Tushar Sharma
2023· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
GPIAtlantic
Robert Moir
2014· book-chapter· en· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Artificial intelligence and public health
K. Lee, B. Gandhi, Jonathan A. Tangsrivimol, Hafeez Ul Hassan Virk, Adham El Sherbini, Zhen Wang +2 more
2025· book-chapter· en· Elsevier eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
EuroGP 2019 panel discussion
Ting Hu, Lukáš Sekanina
2019· article· en· ACM SIGEVOlution· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Complete Platform for Remote Health Management
Bożena Kamińska, Yindar Chuo, M. Marzencki, Benny Hung, Camille Jaggernauth, Kouhyar Tavakolian +1 more
2013· article· en· DOAJ (DOAJ: Directory of Open Access Journals)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
P156 HYPERTENSIVES USERS OF A HEALTH WEB PORTAL
Joany Rousseau Bédard, Lyne Cloutier, André Michaud
2024· article· en· Journal of Hypertension· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
AI-Based Healthcare Chatbot System
Umar Shaikh
2025· article· en· International Journal for Research in Applied Science and Engineering Technology· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About