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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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Health Services Management and Policy
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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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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.

229 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.
229 works in the cohort · of 4,299,418page 1 of 5

Labels cover 0 of 229 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 229 of 229 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.

venueaboutno affunlabeled
Medical marijuana program “a sham”: lawyer.
Patrick S. Sullivan
2002· article· en· Canadian Medical Association Journal· Health Professions
machine prediction:candidate · noneconsensus · none
21
citations
aboutno affunlabeled
Where do the cuts leave the NHS?
Nick Timmins
2010· article· en· BMJ· Health Professions
machine prediction:candidate · noneconsensus · none
7
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
Relieving the pressure?
Tina Donnelly
2008· article· en· Emergency Nurse· Health Professions
machine prediction:candidate · noneconsensus · none
6
citations
aboutno affunlabeled
Hound Pound Narrative
James B. Waldram
2012· book· en· Health Professions
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
So far, so bleak
Daloni Carlisle
2006· article· en· Nursing Older People· Health Professions
machine prediction:candidate · noneconsensus · none
5
citations
affaboutno abstractunlabeled
Health System of Canada
Gregory P. Marchildon, Sara Allin
2023· book-chapter· en· Elsevier eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Betraying the NHS: Health Abandoned
Margot Lindsay
2008· article· en· Journal of Evaluation in Clinical Practice· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Free drugs for all?
Alan Maynard
2009· editorial· en· Journal of the Royal Society of Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
Brexit and EU Competition Policy
Richard Whish
2016· article· en· Journal of European Competition Law & Practice· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About