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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
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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.

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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.

2,822 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 10 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.

affno abstractunlabeled
Paying hypertension research subjects
David Casarett, Jason Karlawish, David A. Asch
2002· article· en· Journal of General Internal Medicine· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
33
citations
affunlabeled
Determining the Impact of Informed Choice
Kirsten McCaffery, Robin Turner, Petra Macaskill, Stephen D. Walter, Siew Foong Chan, Les Irwig
2010· article· en· Medical Decision Making· Medicine
machine prediction:candidate · metaresearchconsensus · none
32
citations
afffundunlabeled
Public variant databases: liability?
Adrian Thorogood, Robert Cook‐Deegan, Bartha Maria Knoppers
2016· article· en· Genetics in Medicine· Medicine
machine prediction:candidate · research_integrityconsensus · none
32
citations
aboutno affunlabeled
Ignorance Is Neither Bliss nor Ethical
John H. Mueller
2007· article· en· Northwestern University law review· Medicine
machine prediction:candidate · research_integrityconsensus · none
32
citations
affunlabeled
Hopes for Helsinki: reconsidering “vulnerability”
Lisa Eckenwiler, Carolyn Ells, Dafna Feinholz, Toby Schonfeld
2008· article· en· Journal of Medical Ethics· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
32
citations
affunlabeled
Offering results to research participants
S. Danielle MacNeil, Conrad V. Fernandez
2006· letter· en· BMJ· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
31
citations
afffundunlabeled
Model consent clauses for rare disease research
Minh Thu Nguyen, Jack Goldblatt, Rosario Isasi, Marlène Jagut, Anneliene Hechtelt Jonker, Petra Kaufmann +7 more
2019· article· en· BMC Medical Ethics· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
31
citations

How this was built: Screen · Findings · About