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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
Abstract
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Study design
Label agreement
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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
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
Evidence
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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 1 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.

affno abstractunlabeled
Guidelines of care for the management of primary cutaneous melanoma
Christopher K. Bichakjian, Allan C. Halpern, Timothy M. Johnson, Antoinette F. Hood, James M. Grichnik, Susan M. Swetter +9 more
2011· article· en· Journal of the American Academy of Dermatology· Medicine
distilled prediction:candidate · noneconsensus · none
616
citations
affunlabeled
Deep features to classify skin lesions
Jeremy Kawahara, Aïcha BenTaieb, Ghassan Hamarneh
2016· article· en· Medicine
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
336
citations
aboutno affunlabeled
Lessons learned from the Sunbelt Melanoma Trial
Kelly M. McMasters, R. Dirk Noyes, Douglas S. Reintgen, James S. Goydos, Peter D. Beitsch, Bradley S. Davidson +3 more
2004· article· en· Journal of Surgical Oncology· Medicine
distilled prediction:candidate · noneconsensus · none
233
citations
affunlabeled
The Performance of MelaFind
Gary D. Monheit, Armand B. Cognetta, Laura K. Ferris, Harold Rabinovitz, Kenneth G. Gross, Mary C. Martini +9 more
2010· article· en· Archives of Dermatology· Medicine
distilled prediction:candidate · noneconsensus · none
229
citations
affno abstractunlabeled
Melanoma in children and adolescents
Alberto S. Pappo
2003· review· en· European Journal of Cancer· Medicine
distilled prediction:candidate · noneconsensus · none
203
citations
afffundno abstractunlabeled
A survey on deep learning for skin lesion segmentation
Zahra Mirikharaji, Kumar Abhishek, Alceu Bissoto, Catarina Barata, Sandra Avila, Eduardo Valle +2 more
2023· review· en· Medical Image Analysis· Medicine
distilled prediction:candidate · insufficient_payloadconsensus · none
178
citations
affunlabeled
Genetic and morphologic features for melanoma classification
Sigrid M. C. Broekaert, Ritu Roy, Ichiro Okamoto, Joost van den Oord, Jürgen Bauer, Claus Garbe +11 more
2010· article· en· Pigment Cell & Melanoma Research· Medicine
distilled prediction:candidate · noneconsensus · none
156
citations
affno abstractunlabeled
Melanoma Epidemiology and Prevention
Marianne Berwick, David B. Buller, Anne Ε. Cust, Richard P. Gallagher, Tim K. Lee, Frank L. Meyskens +4 more
2015· review· en· Cancer treatment and research· Medicine
distilled prediction:candidate · noneconsensus · none
148
citations

How this was built: Screen · Findings · About