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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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AI in cancer detection
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.

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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.

1,463 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.
1,463 works in the cohort · of 4,299,418page 13 of 30

Labels cover 4 of 1,463 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 1,463 of 1,463 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.

afffundunlabeled
Removing radiopaque artifacts from mammograms using area morphology
Michael A. Wirth, Jennifer A. Lyon, Dennis Nikitenko, Alexei Stapinski
2004· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Abstract S4-6: An international Ki67 reproducibility study
TO Nielsen, M.V. Polley, S. Wah Leung, Mauro G. Mastropasqua, LA Zabaglo, JMS Bartlett +4 more
2012· article· en· Cancer Research· Computer Science
machine prediction:candidate · metaresearchconsensus · none
6
citations
fundno affunlabeled
Computational Pathology: A Survey Review and The Way Forward
Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc‐Huy Trinh, Danial Hasan, Xingwen Li, Taehyo Kim +15 more
2023· review· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification
David Clunie, Fred Prior, Michael Rutherford, Stephen Moore, William Parker, Haridimos Kondylakis +8 more
2024· editorial· en· Journal of Imaging Informatics in Medicine· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations

How this was built: Screen · Findings · About