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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 60 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
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
affunlabeled
Value of occupational health research
Lin Fritschi, Peter Smith
2019· editorial· en· Occupational and Environmental Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Basics of Health Technology Assessment
Daria O’Reilly, Kaitryn Campbell, Ron Goeree
2008· review· en· Methods in molecular biology· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
4
citations
affgemma · no categorygpt · no categorymodels split
LIFETIME COSTS FOR MEDICAL SERVICES: A METHODOLOGICAL REVIEW
Philip Jacobs, Kamran Golmohammadi, Teresa Longobardi
2003· review· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
4
citations
aboutno affunlabeled
Pembrolizumab (Keytruda)
CADTH
2023· article· en· Canadian Journal of Health Technologies· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Impact of rarity on Canadian oncology health technology assessment and funding
James Keech, Wei Fang Dai, Maureen Trudeau, Rebecca E. Mercer, Rohini Naipaul, Frances C. Wright +7 more
2020· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
4
citations
afffundunlabeled
Recognizing that Evidence is Made, not Born
Robyn Lim, David K. Lee, Pierre Sabourin, John Ferguson, Marilyn Metcalf, Meredith Y. Smith +13 more
2018· article· en· Clinical Pharmacology & Therapeutics· Economics, Econometrics and Finance
machine prediction:candidate · stsconsensus · none
4
citations
affvenueaboutunlabeled
Quality Review in Psychiatry
Jeffrey P. Reiss, Sarah Jarmain, Kamini Vasudev
2018· article· en· The Canadian Journal of Psychiatry· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Evidence-Based Decision Making 3: Health Technology Assessment
Daria O’Reilly, Richard Audas, Kaitryn Campbell, Meredith Vanstone, James M. Bowen, Lisa Schwartz +2 more
2021· book-chapter· en· Methods in molecular biology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affvenueaboutunlabeled
How to pay for national pharmacare
Michael Wolfson, Steven G. Morgan
2018· article· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Health Utility Values Among Patients With Diabetic Retinopathy, Wet Age-Related Macular Degeneration, and Cataract in Thailand: A Multicenter Survey Using Time Trade-Off, EQ-5D-5L, and Health Utility Index 3
Pear Pongsachareonnont, Phantipa Sakthong, Voraporn Chaikitmongkol, Wantanee Dangboon Tsutsumi, Chavakij Bhoomibunchoo, Cameron Hurst +2 more
2024· article· en· Value in Health Regional Issues· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About