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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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Ethics in Clinical Research
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.

2,822 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.
2,822 works in the cohort · of 4,299,418page 36 of 57

Labels cover 145 of 2,822 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 2,822 of 2,822 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
Adapting and Adaptive Research
Maxwell J. Smith
2024· book-chapter· en· Public health ethics analysis· Medicine
machine prediction:candidate · metaresearchconsensus · none
2
citations
affno abstractunlabeled
Treatment allocation in clinical trials
Kathryn E. Webert
2007· article· en· Transfusion· Medicine
machine prediction:candidate · metaresearchconsensus · metaresearch
2
citations
affno abstractunlabeled
Community Consent for Research on the Impaired Elderly
David C. Thomasma
2001· book-chapter· en· International library of ethics, law, and the new medicine/˜The œinternational library of ethics, law, and the new medicine· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
2
citations
aboutno affunlabeled
Opt out, not opt in.
Mark H. Yudin
2003· letter· en· PubMed· Medicine
machine prediction:candidate · research_integrityconsensus · none
2
citations
aboutno affunlabeled
Addressing the “petty tyranny” of IRBs
Jeffrey R. Botkin
2005· article· en· American Journal of Medical Genetics Part A· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
2
citations
afffundaboutgemma · research_integritygpt · stsmodels split
Research data use in a digital society: a deliberative public engagement
Kim McGrail, Jack Teng, Colene Bentley, Kieran C. O’Doherty, Michael Burgess
2024· article· en· International Journal for Population Data Science· Medicine
machine prediction:candidate · metaresearch+open_scienceconsensus · none
2
citations
affunlabeled
Genomic Data and Privacy
Candace T. Myers, Runjun D. Kumar, Lisa Pilgram, Luca Bonomi, Mara Thomas, Obi L. Griffith +2 more
2025· article· en· Clinical Chemistry· Medicine
machine prediction:candidate · research_integrityconsensus · none
2
citations
affno abstractunlabeled
Biobanks
Bartha Maria Knoppers, Ma’n H. Zawati
2012· book-chapter· en· Elsevier eBooks· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Electronic Health Records
Eike‐Henner W. Kluge
2008· book-chapter· en· IGI Global eBooks· Medicine
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About