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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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Expert Review of Pharmacoeconomics & Outcomes Research
Topic
Retraction
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Study design
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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.

174 results · 1 filter active ·
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20012025
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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.
174 works in the cohort · of 4,299,418page 2 of 4

Labels cover 1 of 174 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 174 of 174 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
Burden of schizophrenia on selected comorbidity costs
Marie‐Hélène Lafeuille, Jason Dean, John Fastenau, Jessica Panish, William H. Olson, Michael A. Markowitz +2 more
2014· article· en· Expert Review of Pharmacoeconomics & Outcomes Research· Medicine
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
How do we value a cure?
Don Husereau
2015· article· en· Expert Review of Pharmacoeconomics & Outcomes Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Patient outcomes after anti TNF-α drugs for Crohn’s disease
Nazila Assasi, Gord Blackhouse, Feng Xie, John K. Marshall, E. Jan Irvine, Kathryn Gaebel +4 more
2010· review· en· Expert Review of Pharmacoeconomics & Outcomes Research· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
15
citations
aboutno affunlabeled
Pharmacoeconomics in oncology
Rebecca Arbuckle, Andrea Adamus, Krista M King
2002· article· en· Expert Review of Pharmacoeconomics & Outcomes Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Cost–effectiveness of rituximab in follicular lymphoma
Karissa Johnston, Corneliu Bolbocean, Joseph M. Connors, Stuart Peacock
2012· review· en· Expert Review of Pharmacoeconomics & Outcomes Research· Medicine
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Cost–effectiveness of therapies for melanoma
Karissa Johnston, Emily McPherson, Katherine M. Osenenko, Joanna Vergidis, Adrian R. Levy, Stuart Peacock
2015· review· en· Expert Review of Pharmacoeconomics & Outcomes Research· Medicine
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
How should we support pharmaceutical innovation?
Paul Grootendorst
2009· article· en· Expert Review of Pharmacoeconomics & Outcomes Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
8
citations
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