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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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Transfusion Medicine
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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
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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.

115 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.
115 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 115 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 115 of 115 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
A survey of the demographics of blood use
T.J. Cobain, Eleftherios C. Vamvakas, A. J. Wells, Kjell Titlestad
2007· review· en· Transfusion Medicine· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
157
citations
affaboutunlabeled
Transfusion practice in elective orthopaedic surgery
Brian G. Feagan, Cindy J. Wong, Catherine Y. Lau, Susan Wheeler, Greg Sue-A-Quan, Alexandra Kirkley
2001· article· en· Transfusion Medicine· Medicine
machine prediction:candidate · noneconsensus · none
79
citations
affunlabeled
Consenting to blood: what do patients remember?
T. K. Chan, Kerena Eckert, P. Venesoen, Ken Leslie, Ian Chin‐Yee
2005· article· en· Transfusion Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
30
citations
affaboutunlabeled
Reaction rates in allogeneic donors
Nadine Shehata, Ryo Kusano, Barbara Hannach, Heather Hume
2004· article· en· Transfusion Medicine· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Qualitative research in transfusion medicine
E. Arnold, Shannon Lane
2011· review· en· Transfusion Medicine· Business, Management and Accounting
machine prediction:candidate · metaresearchconsensus · none
17
citations

How this was built: Screen · Findings · About