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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.

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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
Evidence source
Study design
Label agreement
Label status

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
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 19 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. 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.

affunlabeled
Surgical excision margins for localised cutaneous melanoma
Michael J Sladden, D Barzilai, Anatoli Freiman, Daniel Berg, Teenah Handiside, P.V. Harrison +3 more
2004· reference-entry· en· The Cochrane Database of Systematic Reviews· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Skin Lesions Detection via Convolutional Neural Networks
Zhiyu Wan, Tiansheng Zhang
2021· article· en· 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Skin cancer detection using image processing
N. Aishasiddhika, P. Jenovajosephine
2024· article· en· Louis Savenien Dupuis Journal of Multidisciplinary Research· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Epidemiology of melanoma in rural southern Queensland
Scott Kitchener, Janani Pinidiyapathirage, Keegan Hunter, Lynsey Cochrane, Stephanie Gederts, Toshev SY +8 more
2019· article· en· Australian Journal of Rural Health· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Melanoma Margin Assessment
Martin J. Trotter
2009· review· en· Surgical pathology clinics· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Histological Characteristics of Metastasizing Thin Melanomas
Joan Guitart, Lori Lowe, Michael W. Piepkorn, Víctor G. Prieto, Christopher R. Shea, Victor A. Tron +1 more
2002· article· en· Archives of Dermatology· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
The Risk of Melanoma in Small Congenital Nevi
Irina Turchin, Benjamin Barankin, Joseph G. Morelli
2004· review· en· SKINmed Dermatology for the Clinician· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Metastases of malignant melanoma to stomach
Ufuk Barış Kuzu, Nuretdin Suna, Hale Gökcan, Samir Abdullazade, Erkin Öztaş, Bülent Ödemış
2016· article· en· Gastroenterology Review· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
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How this was built: Screen · Findings · About