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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 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 14 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.

afffundunlabeled
Tackling ethical issues in health technology assessment: A proposed framework
Amanda Burls, Lorraine Caron, Ghislaine Cleret de Langavant, Wybo Dondorp, Christa Harstall, Ela Pathak‐Sen +1 more
2011· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
57
citations
affno abstractunlabeled
Utility Measurement in Healthcare
George W. Torrance
2006· review· en· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
55
citations
affunlabeled
Are preferences over health states complete?
Alan Shiell, J Seymour, Penelope Hawe, Sue Cameron
2000· article· en· Health Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
55
citations
affno abstractunlabeled
The need for increased pragmatism in cardiovascular clinical trials
Muhammad Usman, Harriette G.C. Van Spall, Stephen J. Greene, Ambarish Pandey, Darren K. McGuire, Ziad A. Ali +7 more
2022· review· en· Nature Reviews Cardiology· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
55
citations
affunlabeled
The PROMIS of QALYs
Janel Hanmer, David Feeny, Baruch Fischhoff, Ron D. Hays, Rachel Hess, Paul A. Pilkonis +4 more
2015· article· en· Health and Quality of Life Outcomes· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
55
citations
afffundunlabeled
A Review and Meta-analysis of Colorectal Cancer Utilities
S. Djalalov, Linda Rabeneck, George Tomlinson, Karen E. Bremner, Robert J. Hilsden, Jeffrey S. Hoch
2014· review· en· Medical Decision Making· Economics, Econometrics and Finance
machine prediction:candidate · metaepi_broadconsensus · none
55
citations
affno abstractunlabeled
Priority setting for orphan drugs: An international comparison
Zahava R. S. Rosenberg-Yunger, Abdallah S. Daar, Halla Thorsteinsdóttir, Douglas K. Martin
2010· article· en· Health Policy· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
54
citations
affno abstractunlabeled
Population Norms for SF-6Dv2 and EQ-5D-5L in China
Shitong Xie, Jing Wu, Feng Xie
2022· article· en· Applied Health Economics and Health Policy· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
54
citations
affno abstractunlabeled
Innovations to improve access to musculoskeletal care
Mellick Chehade, Lalit Yadav, Deborah Kopansky-Giles, Mark Merolli, Edward Palmer, Asangi Jayatilaka +1 more
2020· review· en· Best Practice & Research Clinical Rheumatology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
54
citations

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