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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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Blood donation and transfusion practices
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
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.

838 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.
838 works in the cohort · of 4,299,418page 1 of 17

Labels cover 1 of 838 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 838 of 838 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.

affno abstractunlabeled
Challenges in the management of the blood supply
Lorna M. Williamson, Dana V. Devine
2013· review· en· The Lancet· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
240
citations
affunlabeled
Economic Rewards to Motivate Blood Donations
Nicola Lacetera, Mario Macis, Robert Slonim
2013· article· en· Science· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
213
citations
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
affaboutno abstractunlabeled
Pathogen inactivation: making decisions about new technologies
Harvey G. Klein, David R. Anderson, Marie‐Josée Bernardi, Ritchard G. Cable, William Carey, Jeffrey S. Hoch +3 more
2007· article· en· Transfusion· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
104
citations
affunlabeled
The <scp>N</scp>ational <scp>H</scp>eart, <scp>L</scp>ung, and <scp>B</scp>lood <scp>I</scp>nstitute <scp>R</scp>ecipient <scp>E</scp>pidemiology and <scp>D</scp>onor <scp>E</scp>valuation <scp>S</scp>tudy (<scp>REDS</scp>‐<scp>III</scp>): a research program striving to improve blood donor and transfusion recipient outcomes
Steven Kleinman, Michael P. Busch, Edward L. Murphy, Hua Shan, Paul M. Ness, Simone A. Glynn
2013· article· en· Transfusion· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
104
citations
affaboutunlabeled
Forecasting Ontario's blood supply and demand
Adam Drackley, K. Bruce Newbold, Antonio Páez, Nancy M. Heddle
2011· article· en· Transfusion· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
93
citations

How this was built: Screen · Findings · About