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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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Health Systems, Economic Evaluations, Quality of Life
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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.

6,862 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.
6,862 works in the cohort · of 4,299,418page 43 of 138

Labels cover 97 of 6,862 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 6,862 of 6,862 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
Health technology assessment in critical care
Damon C. Scales, Andreas Laupacis
2007· article· en· Intensive Care Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
11
citations
aboutno affunlabeled
CHALLENGES, CHOICES, AND CANADA
Jill M. Sanders
2002· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Explaining odds ratios as conditional risk ratios
Fred M. Hoppe, Daniel J. Hoppe, Stephen D. Walter
2017· letter· en· Journal of Clinical Epidemiology· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
11
citations
fundno affunlabeled
Good Practices for Health Technology Assessment Guideline Development: A Report of the Health Technology Assessment International, HTAsiaLink, and ISPOR Special Task Force
Siobhan Botwright, Manit Sittimart, Kinanti Khansa Chavarina, Diana Beatriz Bayani, Tracy Merlin, Gavin Surgey +5 more
2024· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
11
citations
affunlabeled
Economic burden of cardiovascular disease in the United Kingdom
Naomi Herz, Aziz Sheikh, Ciaran O'Neil, Paul Carter, Michael Anderson
2025· article· en· European Heart Journal - Quality of Care and Clinical Outcomes· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
How to survive the survival plots
Mário L de Lemos
2002· letter· en· The Lancet· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
11
citations
affno abstractunlabeled
Reply to: In defence of simplified PES designs
Sven Wunder, Roy Brouwer, Stefanie Engel, Driss Ezzine‐de‐Blas, Roldán Muradian, Unai Pascual +1 more
2020· article· en· Nature Sustainability· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
11
citations
affaboutunlabeled
The Choosing Wisely Canada cancer initiative.
Gunita Mitera, Andrea Bezjak, Christopher M. Booth, Guila Delouya, Christine Desbiens, Craig C. Earle +5 more
2014· article· en· Journal of Clinical Oncology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
11
citations
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