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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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Ultrasound in Clinical Applications
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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.

affaffiliation
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venuejournal
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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.

1,606 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.
1,606 works in the cohort · of 4,299,418page 7 of 33

Labels cover 6 of 1,606 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 1,606 of 1,606 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
Better With Ultrasound
Scott J. Millington, Seth Koenig
2017· article· en· CHEST Journal· Medicine
machine prediction:candidate · noneconsensus · none
30
citations
venueno affunlabeled
Machine Learning in Point of Care Ultrasound
Momodou L. Sonko, Thomas Arnold, Ivan A. Kuznetsov
2022· review· en· POCUS Journal· Medicine
machine prediction:candidate · noneconsensus · none
29
citations
affaboutunlabeled
Telementorable "just-in-time" lung ultrasound on an iPhone
AndrewW Kirkpatrick, Innes Crawford, PaulB McBeth, Mark Mitchelson, Corina Tiruta, James Ferguson
2011· article· en· Journal of Emergencies Trauma and Shock· Medicine
machine prediction:candidate · noneconsensus · none
29
citations
affno abstractunlabeled
Better With Ultrasound
Atul Jaidka, Hailey Hobbs, Seth Koenig, Scott J. Millington, Robert Arntfield
2018· review· en· CHEST Journal· Medicine
machine prediction:candidate · noneconsensus · none
29
citations
affno abstractunlabeled
Critical Care Ultrasonography
Robert Arntfield
2014· article· en· Emergency Medicine Clinics of North America· Medicine
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Cardiac Auscultation in the Modern Era
Michael Barrett, Andrew S. Mackie, J. P. Finley
2017· article· en· Cardiology in Review· Medicine
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
Thinking Critically About Appraising <scp>FOAM</scp>
Teresa M. Chan, Anuja Bhalerao, Brent Thoma, N. Seth Trueger, Andrew Grock
2019· article· en· AEM Education and Training· Medicine
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
Thoracic Ultrasound
Joel Turner, Jerrald Dankoff
2012· review· en· Emergency Medicine Clinics of North America· Medicine
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
Better With Ultrasound
Scott J. Millington, Marcos Silva Restrepo, Seth Koenig
2018· review· en· CHEST Journal· Medicine
machine prediction:candidate · noneconsensus · none
26
citations
affvenueunlabeled
Teaching Point-of-Care Ultrasound in Medicine
Andrew A. Moses, Willy Weng, Ani Orchanian‐Cheff, Rodrigo B. Cavalcanti
2020· article· en· Canadian Journal of General Internal Medicine· Medicine
machine prediction:candidate · noneconsensus · none
26
citations

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