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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.

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.

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 13 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.

affunlabeled
A history of health technology assessment at the European level
David Banta, Finn Børlum Kristensen, Egon Jonsson
2009· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
62
citations
fundno affunlabeled
Quality of Life Before Intensive Care Using EQ-5D
Victor D. Dinglas, Jeneen M. Gifford, Nadia Husain, Elizabeth Colantuoni, Dale M. Needham
2012· article· en· Critical Care Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
62
citations
affno abstractunlabeled
Why it’s Time to Abandon the ICER
Mike Paulden
2020· article· th· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
62
citations
affno abstractunlabeled
QALYs
Maurice McGregor, J. Jaime
2006· article· en· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
62
citations
affno abstractunlabeled
Transparency in Decision Modelling: What, Why, Who and How?
Chris Sampson, Renée J.G. Arnold, Stirling Bryan, Philip Clarke, Sean Ekins, Anthony J. Hatswell +7 more
2019· review· en· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
61
citations
affunlabeled
Impact of the COVID-19 Pandemic on Non-COVID-19 Clinical Trials
Katia Audisio, Hillary Lia, N. Bryce Robinson, Mohamed Rahouma, Giovanni Soletti, Gianmarco Cancelli +8 more
2022· article· en· Journal of Cardiovascular Development and Disease· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
59
citations
affno abstractunlabeled
Incorporation of uncertainty in health economic modelling studies
Anthony O’Hagan, Christopher McCabe, Ron Akehurst, Alan Brennan, Andrew Briggs, Karl Claxton +5 more
2005· article· en· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
59
citations
aboutno affunlabeled
State of rare disease management in Southeast Asia
Asrul Akmal Shafie, Nathorn Chaiyakunapruk, Azuwana Supian, Jeremy Fung Yen Lim, Matt Zafra, Mohamed Azmi Hassali
2016· article· en· Orphanet Journal of Rare Diseases· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
59
citations
affunlabeled
GRADE EVIDENCE TO DECISION (EtD) FRAMEWORK FOR COVERAGE DECISIONS
Elena Parmelli, Laura Amato, Andrew D Oxman, Pablo Alonso‐Coello, Massimo Brunetti, Jenny Moberg +5 more
2017· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
58
citations
afffundaboutno abstractunlabeled
Ontario??s Formulary Committee
Anne M PausJenssen, Peter Singer, Allan S. Detsky
2003· article· en· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
58
citations
affno abstractunlabeled
The unequal burden of time toxicity
Whitney Victoria Johnson, Anne Blaes, Christopher M. Booth, Ishani Ganguli, Arjun Gupta
2023· article· en· Trends in cancer· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
58
citations
afffundunlabeled
BREAKING THE ADDICTION TO TECHNOLOGY ADOPTION
Stirling Bryan, Craig Mitton, Cam Donaldson
2014· editorial· en· Health Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
57
citations

How this was built: Screen · Findings · About