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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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COVID-19 Clinical Research Studies
Retraction
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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
fundfunder
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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.

2,625 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,625 works in the cohort · of 4,299,418page 3 of 53

Labels cover 25 of 2,625 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 2,625 of 2,625 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.

affunlabeled
T cell apoptosis characterizes severe Covid-19 disease
Sónia André, Morgane Picard, Renaud Cezar, Florence Roux‐Dalvai, Aurélie Alleaume‐Butaux, Calaiselvy Soundaramourty +27 more
2022· article· en· Cell Death and Differentiation· Medicine
machine prediction:candidate · noneconsensus · none
185
citations
afffundunlabeled
Comparative ACE2 variation and primate COVID-19 risk
Amanda Melin, Mareike C. Janiak, Frank Marrone, Paramjit S. Arora, James P. Higham
2020· article· en· Communications Biology· Medicine
machine prediction:candidate · noneconsensus · none
180
citations
afffundno abstractunlabeled
ISTH guidelines for antithrombotic treatment in COVID‐19
Sam Schulman, Michelle Sholzberg, Alex C. Spyropoulos, Ryan Zarychanski, Helaine E. Resnick, Charlotte Bradbury +12 more
2022· article· en· Journal of Thrombosis and Haemostasis· Medicine
machine prediction:candidate · noneconsensus · none
179
citations
affunlabeled
COVID-19 and Respiratory System Disorders
Shari B. Brosnahan, Annemijn H. Jonkman, Matthias C. Kugler, John S. Munger, David A. Kaufman
2020· review· en· Arteriosclerosis Thrombosis and Vascular Biology· Medicine
machine prediction:candidate · noneconsensus · none
179
citations
affunlabeled
Platelets Promote Thromboinflammation in SARS-CoV-2 Pneumonia
Francesco Taus, Gian Luca Salvagno, Stefania Canè, Cristiano Fava, Fulvia Mazzaferri, Elena Carrara +14 more
2020· article· en· Arteriosclerosis Thrombosis and Vascular Biology· Medicine
machine prediction:candidate · noneconsensus · none
176
citations
affno abstractunlabeled
Therapeutic advances in COVID-19
Naoka Murakami, Robert Hayden, Thomas Hills, Hanny Al‐Samkari, Jonathan D. Casey, Lorenzo Del Sorbo +3 more
2022· review· en· Nature Reviews Nephrology· Medicine
machine prediction:candidate · noneconsensus · none
158
citations
afffundunlabeled
Novel insights on the pulmonary vascular consequences of COVID-19
François Potus, Vicky Mai, Marius Lebret, Simon Malenfant, Émilie Breton-Gagnon, Annie C. Lajoie +3 more
2020· review· en· American Journal of Physiology-Lung Cellular and Molecular Physiology· Medicine
machine prediction:candidate · noneconsensus · none
155
citations

How this was built: Screen · Findings · About