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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.

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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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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.

2,802 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.
2,802 works in the cohort · of 4,299,418page 31 of 57

Labels cover 38 of 2,802 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 2,802 of 2,802 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
Septic arthritis in children
Andrew Howard, M. Wilson
2010· article· en· BMJ· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
aboutno affunlabeled
The health crisis in Russia
Rifat Atun
2005· editorial· en· BMJ· Health Professions
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Transparency in clinical trial reporting
Paula A. Rochon, Nathan M. Stall, Rachel Savage, An‐Wen Chan
2018· letter· en· BMJ· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearchconsensus · none
8
citations
affno abstractunlabeled
Patient and public involvement in research reporting
Sophie Staniszewska, Sally Hopewell, Dawn P. Richards, Runcie C W Chidebe
2025· editorial· en· BMJ· Health Professions
machine prediction:candidate · metaresearchconsensus · none
8
citations
aboutno affunlabeled
Spread of SARS slows
J. Parry
2003· article· de· BMJ· Health Professions
machine prediction:candidate · noneconsensus · none
8
citations
aboutno affunlabeled
Randomised trials in surgery
Rajan Madhok
2002· letter· en· BMJ· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
affaboutno abstractunlabeled
Canada finally opens up data on new drugs and devices
Joel Lexchin, Matthew Herder, Peter Doshi
2019· editorial· en· BMJ· Economics, Econometrics and Finance
machine prediction:candidate · metaresearch+open_scienceconsensus · none
8
citations
affunlabeled
Statins, risk, and personalised care
Samuel Finnikin, Brian Finney, Rani Khatib, James McCormack
2024· article· en· BMJ· Medicine
machine prediction:candidate · noneconsensus · none
8
citations
aboutno affunlabeled
Challenges of private provision in the NHS
Catherine Guly, Richard Sidebottom, K N Hakin, Keith Bates
2005· letter· en· BMJ· Health Professions
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
HIV pre-exposure prophylaxis (PrEP)
Ethan Tumarkin, Mark J. Siedner, Isaac I. Bogoch
2019· article· fr· BMJ· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
Learning from indigenous people
R. Smith
2003· article· en· BMJ· Decision Sciences
machine prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
Women too busy to exercise
Jocalyn Clark
2003· article· en· BMJ· Medicine
machine prediction:candidate · noneconsensus · none
7
citations
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