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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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npj Digital Medicine
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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.

214 results · 1 filter active ·
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20182025
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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.
214 works in the cohort · of 4,299,418page 2 of 5

Labels cover 0 of 214 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 214 of 214 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundunlabeled
Virtual care models for cancer survivorship
Quỳnh Phạm, Jason Hearn, Bruce Gao, Ian Brown, Robert J. Hamilton, Alejandro Berlín +2 more
2020· review· en· npj Digital Medicine· Medicine
distilled prediction:candidate · metaepi_narrowconsensus · none
54
citations
fundno affunlabeled
U-Sleep’s resilience to AASM guidelines
Luigi Fiorillo, Giuliana Monachino, Julia van der Meer, Marco Pesce, Jan D. Warncke, Markus H. Schmidt +4 more
2023· article· en· npj Digital Medicine· Medicine
distilled prediction:candidate · insufficient_payloadconsensus · none
46
citations
fundno affunlabeled
Lymelight: forecasting Lyme disease risk using web search data
Adam Sadilek, Yulin Hswen, Shailesh Bavadekar, Tomer Shekel, John S. Brownstein, Evgeniy Gabrilovich
2020· article· en· npj Digital Medicine· Medicine
distilled prediction:candidate · metaepi_narrowconsensus · none
27
citations
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How this was built: Screen · Findings · About