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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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Journal of Pathology Informatics
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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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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

72 results · 1 filter active ·
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20112025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
72 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 72 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 72 of 72 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
Review of the current state of whole slide imaging in pathology
Liron Pantanowitz, Paul N. Valenstein, Andrew Evans, Keith J. Kaplan, John D. Pfeifer, David C. Wilbur +2 more
2011· article· en· Journal of Pathology Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
432
citations
afffundunlabeled
Computational pathology: A survey review and the way forward
Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc‐Huy Trinh, Lyndon Chan, Danial Hasan, Xingwen Li +15 more
2024· review· en· Journal of Pathology Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
96
citations
affunlabeled
Colorectal Cancer Detection Based on Deep Learning
Lin Xu, Blair Walker, Peir‐In Liang, Yi Xin Tong, Cheng Xu, Yu Su +1 more
2020· article· en· Journal of Pathology Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
83
citations
affunlabeled
The history of pathology informatics: A global perspective
Seung Park, Anil V. Parwani, Raymond D. Aller, L Banach, Michael J. Becich, Stephan Borkenfeld +12 more
2013· article· en· Journal of Pathology Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
70
citations
affunlabeled
Performance of CellaVision DM96 in leukocyte classification
Lik Hang Lee, Adnan Mansoor, Brenda Wood, Heather H. Nelson, Diane Higa, Christopher Naugler
2013· article· en· Journal of Pathology Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
affaboutunlabeled
A real-time dashboard for managing pathology processes
Fawaz Halwani, Wei Chen Li, Diponkar Banerjee, Lysanne Lessard, Daniel Amyot, Wojtek Michalowski +1 more
2016· article· en· Journal of Pathology Informatics· Medicine
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
2020 Vision of Digital Pathology in Action
L. Sylvia, Anna Bodén, Darren Treanor, Sofia Jarkman, Claes Lundström, Liron Pantanowitz
2019· editorial· en· Journal of Pathology Informatics· Medicine
machine prediction:candidate · noneconsensus · none
12
citations

How this was built: Screen · Findings · About