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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.

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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Cardiac Imaging and Diagnostics
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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.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,172 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,172 works in the cohort · of 4,299,418page 25 of 64

Labels cover 6 of 3,172 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 3,172 of 3,172 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.

afffundno abstractunlabeled
Are Training Programs Ready for the Rapid Adoption of CCTA?
Benjamin J.W. Chow, Yeung Yam, Ali Alenazy, Andrew Crean, Owen Clarkin, Alomgir Hossain +1 more
2021· article· en· JACC. Cardiovascular imaging· Medicine
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Comparison of dual-bolus versus dual-sequence techniques for determining myocardial blood flow and myocardial perfusion reserve by cardiac magnetic resonance stress perfusion: From the Automated Quantitative analysis of myocardial perfusion cardiac Magnetic Resonance Consortium
Emily Yin Sing Chong, Haonan Wang, Kwan Ho Gordon Leung, Paul Kim, Yuko Tada, Tsun Hei Sin +7 more
2024· article· en· Journal of Cardiovascular Magnetic Resonance· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Cardiovascular Magnetic Resonance Imaging
Raymond Y. Kwong, Michael Jerosch‐Herold, Bobak Heydari
2019· book· en· Contemporary cardiology· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Comprehensive review of artifacts in cardiac MRI and their mitigation
Moezedin Javad Rafiee, Katerina Eyre, Margherita Leo, Mitchel Benovoy, Matthias G. Friedrich, Michael Chetrit
2024· review· en· The International Journal of Cardiovascular Imaging· Medicine
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About