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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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Cutaneous Melanoma Detection and Management
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
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.

1,482 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.
1,482 works in the cohort · of 4,299,418page 6 of 30

Labels cover 3 of 1,482 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 1,482 of 1,482 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

aboutno affunlabeled
What’s a Cellphilm?
2016· book· de· SensePublishers eBooks· Medicine
distilled prediction:candidate · metaepi_narrow+scholarly_communication+research_integrity+insufficient_payloadconsensus · metaepi_narrow+insufficient_payload
37
citations
affno abstractunlabeled
Knowledge distillation approach towards melanoma detection
Md Shakib Khan, Kazi Nabiul Alam, Abdur Rab Dhruba, Hasib Zunair, Nabeel Mohammed
2022· article· en· Computers in Biology and Medicine· Medicine
distilled prediction:candidate · noneconsensus · none
36
citations
affunlabeled
Rare Presentations of Primary Melanoma and Special Populations
Lisa A. Kottschade, Travis E. Grotz, Roxana Dronca, Diva R. Salomão, José S. Pulido, Nabil Wasif +16 more
2013· review· en· American Journal of Clinical Oncology· Medicine
distilled prediction:candidate · noneconsensus · none
35
citations
affaboutunlabeled
Sun Exposure and Melanoma Survival: A GEM Study
Marianne Berwick, Anne S. Reiner, Susan Paine, Bruce K. Armstrong, Anne Kricker, Chris Goumas +17 more
2014· article· en· Cancer Epidemiology Biomarkers & Prevention· Medicine
distilled prediction:candidate · noneconsensus · none
33
citations
affno abstractunlabeled
Machine learning and melanoma: The future of screening
Tyler Safran, Alex Viezel-Mathieu, Jason Corban, Ari Kanevsky, Stéphanie Thibaudeau, Jonathan Kanevsky
2017· review· en· Journal of the American Academy of Dermatology· Medicine
distilled prediction:candidate · noneconsensus · none
30
citations
affno abstractunlabeled
A retrospective multicenter study of fatal pediatric melanoma
Elena B. Hawryluk, Danna Moustafa, Diana W. Bartenstein, Meera Brahmbhatt, Kelly M. Cordoro, Laura Gardner +19 more
2020· article· en· Journal of the American Academy of Dermatology· Medicine
distilled prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Counting Moles Automatically From Back Images
Tim K. Lee, M. Stella Atkins, Michael A. King, S. Lau, David I. McLean
2005· article· en· IEEE Transactions on Biomedical Engineering· Medicine
distilled prediction:candidate · insufficient_payloadconsensus · none
29
citations

How this was built: Screen · Findings · About