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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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Global Cancer Incidence and Screening
Retraction
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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
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venuejournal
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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.

2,608 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.
2,608 works in the cohort · of 4,299,418page 31 of 53

Labels cover 12 of 2,608 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 2,608 of 2,608 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
Reply to Kopans
Steven A. Narod
2017· letter· en· Breast Cancer Research and Treatment· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Importance of accessible cancer care
Pamela Skrabek
2013· review· en· Transfusion and Apheresis Science· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affvenueno abstractunlabeled
Don't Just Do Something, Stand There
George Carson
2016· editorial· en· Journal of Obstetrics and Gynaecology Canada· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affvenueaboutunlabeled
Addressing the Rising Trend in Early-Age-Onset Cancers in Canada
Petra Wildgoose, Filomena Servidio-Italiano, Michael J. Raphael, Monika Slovinec D’Angelo, Cassandra Macaulay, Shaqil Kassam +9 more
2024· article· en· Current Oncology· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Seasonal Variation in Inflammatory Breast Cancer
Levine Ph, Yisi Liu, Carmela C. Veneroso, Sohaib Hashmi, M Cristofanilli
2016· article· en· International Journal of Virology Studies & Research· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
DCIS and invasive interval breast cancer
Steven A. Narod, Victoria Sopik, Ping Sun
2016· letter· en· The Lancet Oncology· Medicine
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About