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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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Pharmaceutical industry and healthcare
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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.

1,489 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.
1,489 works in the cohort · of 4,299,418page 8 of 30

Labels cover 35 of 1,489 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 1,489 of 1,489 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
Financial conflicts of interest in clinical research
Jelena Savović, Elie A. Akl, Asbjørn Hróbjartsson
2018· editorial· en· Intensive Care Medicine· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearch+research_integrityconsensus · none
13
citations
affunlabeled
A primer on project management
Victor Maddalena
2012· article· en· Leadership in health services· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
13
citations
afffundunlabeled
Gender bias in cardiovascular advertisements
Sofia B. Ahmed, Sherry L. Grace, Henry T. Stelfox, George Tomlinson, Angela M. Cheung
2004· article· en· Journal of Evaluation in Clinical Practice· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
The FDA’s new clothes
Donald W. Light, Joel Lexchin
2015· letter· en· BMJ· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
12
citations
venueaboutno affunlabeled
Canadian Drug Regulatory Framework
Libusha Kelly, Michele Di Lazzaro, C. Petersen
2007· article· en· Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Industry Payments to American Editorial Board Members of Major Urology Journals
David‐Dan Nguyen, Liam Murad, Anne Xuan-Lan Nguyen, Anudari Zorigtbaatar, David Bouhadana, Claudia Deyirmendjian +8 more
2023· letter· en· European Urology· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearch+research_integrityconsensus · none
11
citations
venueaboutno affunlabeled
Pharma influence widespread at medical schools: study
Wendy Glauser
2013· article· en· Canadian Medical Association Journal· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearchconsensus · none
11
citations
affvenueunlabeled
Money for nothing and your referees for free
Christopher J. Lortie
2011· article· en· Ideas in Ecology and Evolution· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
11
citations

How this was built: Screen · Findings · About