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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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Healthcare Policy and Management
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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
fundfunder
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.

3,600 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.
3,600 works in the cohort · of 4,299,418page 62 of 72

Labels cover 5 of 3,600 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 3,600 of 3,600 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
Ten Years for GHSP: Where Are We Now? Where Will We Go?
Zulfiqar A Bhutta, Rajani Ved, Nana Twum-Danso, Abdulmumin Saad, Stephen Hodgins
2023· letter· en· Global Health Science and Practice· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Transferring patients between facilities and the ED
Cathy Sendecki
2011· article· en· Canadian Journal of Emergency Nursing· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
SENIOR HIGH COST HEALTHCARE USERS: HOW DO THEY DIFFER?
Janice Lee, S. Muratov, Jean‐Éric Tarride, Michael J. Paterson, Tara Gomes, Wayne Khuu +1 more
2017· article· en· Innovation in Aging· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
BC First Nations to run own health system
Laurent Vogel
2011· article· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Accountability lacking under health accord
Laurent Vogel
2011· article· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
The Cost of Staying Alive: Healthcare in America
Blake Benson
2020· article· en· ValpoScholar (Valparaiso University)· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Letters to the Editor
Barbara S. Schneidman
2022· article· en· Journal of Medical Regulation· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Mega purchasing leads to a mega mess
Edward J. Harvey
2015· editorial· en· Canadian Journal of Surgery· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Letters
R. John Hurlbert, David Alexander, Stewart I. Bailey, James Mahood, E. P. Abraham, Robert McBroom +2 more
2014· letter· sl· Spine· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutunlabeled
Letter to the Editor
James G. Wright
2003· letter· en· Journal of Pediatric Orthopaedics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Tax Credits and the Use of Medical Care
Michael Smart, Mark Stabile
2003· article· en· National Bureau of Economic Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Recessions and Admissions to Substance Abuse Treatment
Jonathan Cantor, Brady P. Horn, Johanna Catherine Maclean
2013· report· en· National Bureau of Economic Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
When Patients Lose Coverage, Clinicians Lose Heart
Pamela F. Cipriano, Jerry P. Abraham, Nikitha Balaji, Donald M. Berwick, Jackie Gerhart, Marc B. Hahn +4 more
2025· article· en· NAM Perspectives· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About