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
Heart Rate Variability and Autonomic Control
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.

2,542 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.
2,542 works in the cohort · of 4,299,418page 43 of 51

Labels cover 3 of 2,542 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 2,542 of 2,542 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.

aboutno affunlabeled
Сравнительный анализ изменения толерантности к физической нагрузке у больных ишемической болезнью сердца под влиянием терапии, основанной на бисопрололе и ивабрадине
Е А Недоруба, А. Д. Багмет, Т В Таютина
2013· article· ru· Современные проблемы науки и образования· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
afffundno abstractunlabeled
Reply: Complexity Analysis of Respiratory Dynamics
Cindy Thamrin, Urs Frey, David A. Kaminsky, Helen K. Reddel, Andrew Seely, Bélâ Suki +1 more
2017· letter· en· American Journal of Respiratory and Critical Care Medicine· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
NASA TOPS Open Science 101
2023· other· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · open_science+insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
HEART FAILURE AFTER LABORATORY CONFIRMED INFLUENZA INFECTION
Phyllis Sin, Muhammad Ilyas Siddiqui, R Woźniak, Jessica Minion, Stephen Sanche, Jacob A. Udell +2 more
2020· article· en· Journal of the American College of Cardiology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
TMS-EEG Biomarkers of Suicidal Ideation
Noah Stapper, Yinming Sun, Mohsen Poorganji, Itay Hadas, Reza Zomorrodi, Paul B. Fitzgerald +4 more
2025· article· fr· Brain stimulation· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About