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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
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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 37 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
Commentary on Berlin et al.
Dean Fergusson, Paul C. Hébert
2014· letter· en· Clinical Trials· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Can synthetic data accurately mimic oncology clinical trials?
Samer El Kababji, Nicholas Mitsakakis, Xi Fang, Ana-Alicia Beltran-Bless, Gregory R. Pond, Lisa Vandermeer +4 more
2023· article· en· Journal of Clinical Oncology· Medicine
machine prediction:candidate · metaresearchconsensus · none
2
citations
affunlabeled
Research Ethics in Epidemics and Pandemics: A Casebook
Susan Bull, Michael Parker, Joseph Ali, Monique Jonas, Vasantha Muthuswamy, Carla Saénz +4 more
2024· book· en· Public health ethics analysis· Medicine
machine prediction:candidate · research_integrityconsensus · none
2
citations
aboutno affunlabeled
Finding Respondents from Minority Groups
Nelda Mier, Alvaro A. Medina, Anabel Bocanegra-Alonso, Octelina Castillo-Ruíz, Rosa Issel Acosta‐González, José A. Ramı́rez
2007· article· en· Medicine
machine prediction:candidate · metaresearchconsensus · none
2
citations
affvenueno abstractunlabeled
It takes a village…
2013· letter· en· Canadian Journal of Public Health· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
The Oliveri Case: Lessons for Australasia
2005· article· en· eCite Digital Repository (University of Tasmania)· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Informed Consent
Michèle Stanton-Jean, Hubert Doucet, Thérèse Leroux
2013· book-chapter· zh· Medicine
machine prediction:candidate · research_integrityconsensus · none
2
citations
venueno affunlabeled
Frozen in Translation: Biobanks as a Tool for Cancer Research
Ana Teresa Martins, Isa Carneiro, Sara Monteiro‐Reis, João Lobo, Ana Luís, Cármen Jerónimo +1 more
2015· article· en· Journal of Intellectual Disability - Diagnosis and Treatment· Medicine
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Communicating clearly about data sharing in genomics
Donrich Thaldar, Diya Uberoi, Adrian Thorogood, Richard Milne, Ainsley J. Newson, Alison Hall +3 more
2025· review· en· Human Genomics· Medicine
machine prediction:candidate · metaresearch+open_scienceconsensus · none
2
citations
venueno affunlabeled
Correction
2009· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About