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
T-cell and B-cell Immunology
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.

2,101 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.
2,101 works in the cohort · of 4,299,418page 8 of 43

Labels cover 2 of 2,101 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 2,101 of 2,101 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
Knockout mice: a paradigm shift in modern immunology
Tak W. Mak, Josef Penninger, Pamela S. Ohashi
2001· review· en· Nature reviews. Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
78
citations
affno abstractunlabeled
Trogocytosis of CD80 and CD86 by induced regulatory T cells
Peng Gu, Julia Fang Gao, Cheryl D’Souza, Aleksandra Kowalczyk, Kuang‐Yen Chou, Li Zhang
2012· article· en· Cellular and Molecular Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
77
citations
affno abstractunlabeled
Expression of CD103 identifies human regulatory T-cell subsets
Zoulfia Allakhverdi, Annie Boisvert, Nobuyasu Baba, Salim Bouguermouh, G Delespesse
2006· article· en· Journal of Allergy and Clinical Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
76
citations
affno abstractunlabeled
Methylation protects cytidines from AID-mediated deamination
Mani Larijani, Darina Frieder, Timothy M. Sonbuchner, Ronda Bransteitter, Michael Goodman, Eric E. Bouhassira +2 more
2004· article· en· Molecular Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
74
citations
affno abstractunlabeled
Negative selection and autoimmunity
Pamela S. Ohashi
2003· review· en· Current Opinion in Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
72
citations
affno abstractunlabeled
Effector lymphocytes in autoimmunity
Pere Santamaría
2001· review· en· Current Opinion in Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
72
citations
affunlabeled
HLA Peptide Length Preferences Control CD8+ T Cell Responses
Melissa J. Rist, Alex Theodossis, Nathan P. Croft, Michelle A. Neller, Andrew Welland, Zhenjun Chen +11 more
2013· article· en· The Journal of Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
70
citations
afffundunlabeled
Phylogenetic Analysis of the MS4A and TMEM176 Gene Families
Jonathan Zuccolo, Jeremy A. Bau, Sarah J. Childs, Greg G. Goss, Christoph W. Sensen, Julie P. Deans
2010· article· en· PLoS ONE· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
70
citations
affunlabeled
Immunosupportive therapies in aging
Tamàs Fülöp, Anis Larbi, Katsuiku Hirokawa, Eugenio Mocchegiani, Bruno Lesourds, Stephen Castle +3 more
2007· review· en· Clinical Interventions in Aging· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
70
citations
affno abstractunlabeled
Mechanisms of antigen receptor evolution
Donna D. Eason, John P. Cannon, Robert N. Haire, Jonathan P. Rast, David A. Ostrov, Gary W. Litman
2004· review· en· Seminars in Immunology· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
69
citations
afffundno abstractunlabeled
Genetic background of multiple sclerosis
A. Dessa Sadovnick
2011· review· en· Autoimmunity Reviews· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
68
citations

How this was built: Screen · Findings · About