MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Cancer Research and Treatments
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.

698 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
698 works in the cohort · of 4,299,418page 7 of 14

Labels cover 2 of 698 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 698 of 698 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.

affno abstractunlabeled
CIFAR humans and the microbiome: Banff meeting report
Michael Hunter, Melissa K. Melby, B. Brett Finlay
2023· article· en· Trends in Microbiology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Modeling Therapy of Late or Early‐Stage Metastatic Disease in Mice
Robert S. Kerbel, Marta Pàez‐Ribes, Shan Man, Ping Xu, Éric Guérin, William Cruz‐Muñoz +1 more
2017· other· en· Holland‐Frei Cancer Medicine· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Raw CyTOF images associated with Moldoveanu et al. 2022, Science Immunology
Dan Moldoveanu, LeeAnn Ramsay, Mathieu Lajoie, Luke Anderson-Trocmé, Marine Lingrand, Diana Berry +35 more
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
Caulobacter in cancer immunotherapy
Mayowa Adeleye, Pravin K. Bhatnagar, Mavanur R. Suresh, John Smit
2007· article· en· Clinical Cancer Research· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Preliminary Results of A Phase I/IIA Study of BMS-986156 (GLUCOCORTICOID-Induced Tumour Necrosis Factor Receptor-Related Gene [GITR] AGONIST), Alone and In Combination with Nivolumab in Patients with Advanced Solid Tumours
Lillian L. Siu, Neeltje Steeghs, Tarek Meniawy, Markus Joerger, Jennifer L. Spratlin, Sylvie Rottey +10 more
2017· article· en· UWA Profiles and Research Repository (University of Western Australia)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About