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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.

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.

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

affunlabeled
Medical Outcome Prediction for Intensive Care Unit Patients
Simone A. Ludwig, Stefanie Roos, Monique Frize, Nicole Yu
2010· article· en· International Journal of Computational Models and Algorithms in Medicine· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
AI Chaperone: Awareness Chatbot for Alzheimer’s Disease
Su. Suganthi, Mr. Nithish Kumar R, Mr. Logeswaran S R, Mr. Rohan Kumar M, Mr. Hariprasad V
2024· article· en· International Research Journal on Advanced Engineering and Management (IRJAEM)· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study
Yukiko Ohno, Tohru Aomori, Tomohiro Nishiyama, Riri Kato, Reina Fujiki, Haruki Ishikawa +5 more
2025· article· en· JMIR Medical Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About