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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 59 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.

affaboutunlabeled
MEDICAL DEVICE PRICES IN ECONOMIC EVALUATIONS
İlke Akpinar, Philip Jacobs, Don Husereau
2015· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
5
citations
afffundunlabeled
Ultra-orphan drugs: can we afford the price
Devidas Menon, Tania Stafinski
2017· article· en· Expert Opinion on Orphan Drugs· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
5
citations
aboutno affunlabeled
AOTMiT reimbursement recommendations compared to other HTA agencies
Aneta Mela, Dorota Lis, Elżbieta Rdzanek, Janusz Jaroszyński, Marzena Furtak-Niczyporuk, Bartłomiej Drop +2 more
2024· article· en· The European Journal of Health Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
HOSPITAL-BASED HEALTH TECHNOLOGY ASSESSMENT IN IRAN
Farideh Mohtasham, Reza Majdzadeh, Ensiyeh Jamshidi
2017· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
WHO COVID-19 therapeutic guidelines
Bram Rochwerg, Thomas Agoritsas, Janet Dı́az, Lisa Askie
2021· letter· en· The Lancet· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
ACNP 58th Annual Meeting: Poster Session II
Clifford Cassidy, Seyda Celebi, Mélissa Savard, Mira Chamoun, Christine Tardif, Pedro Rosa‐Neto +30 more
2019· article· en· Neuropsychopharmacology· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
5
citations

How this was built: Screen · Findings · About