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
Lung Cancer Diagnosis and Treatment
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.

2,572 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.
2,572 works in the cohort · of 4,299,418page 34 of 52

Labels cover 12 of 2,572 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 2,572 of 2,572 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.

affaboutunlabeled
Lung Cancer in Elderly
Anne Dagnault, Jean Archambault
2012· book-chapter· en· InTech eBooks· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Risk-Adapted Lung SBRT for Central and Ultra-Central Tumors
Alexis Lenglet, Dominique Mathieu, Marie‐Pierre Campeau, Houda Bahig, T. Vu, David Roberge +2 more
2017· article· en· International Journal of Radiation Oncology*Biology*Physics· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Response to Dr. Røe’s comment letter
Martin C. Tammemägi
2019· letter· en· Translational Lung Cancer Research· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Just Because We Can, Does Not Always Mean We Should
Lakshmi Mudambi, George A. Eapen, Kazuhiro Yasufuku
2015· editorial· en· Journal of Bronchology & Interventional Pulmonology· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Rebuttal from Dr. Bezjak and Dr. Giuliani
Meredith Giuliani, Andrea Bezjak
2016· editorial· en· Translational Lung Cancer Research· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
19-G EBUS: Why, When, and How?
Alain Tremblay, Christopher A. Hergott
2018· editorial· en· Journal of Bronchology & Interventional Pulmonology· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Thoracic imaging
Jonathan Yeung, Laura Donahoe, Ricarda Hinzpeter, Patrick Veit‐Haibach
2022· book-chapter· en· Elsevier eBooks· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations

How this was built: Screen · Findings · About