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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 19 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
Building clinically relevant outcomes across the Alzheimer's disease spectrum
Dorene M. Rentz, Alette M. Wessels, Ananth Annapragada, Anna‐Karin Berger, Chris J. Edgar, Michael Gold +11 more
2021· article· en· Alzheimer s & Dementia Translational Research & Clinical Interventions· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
40
citations
afffundno abstractunlabeled
Assessing Health Utilities in Schizophrenia
Lakshmi N.P. Voruganti, A. George Awad, L. Kola Oyewumi, Leonardo Cortese, Sandra Zirul, Ravinder Dhawan
2000· article· en· PharmacoEconomics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
40
citations
aboutno affunlabeled
Pharmacoeconomics: From Theory to Practice
David A. Holdford
2010· article· en· PubMed Central· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
40
citations
affno abstractunlabeled
Mapping utility scores from the Barthel index
Billingsley Kaambwa, Lucinda Billingham, Stirling Bryan
2011· article· en· The European Journal of Health Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
The Half-Cycle Correction Revisited
David Naimark, Nader N. Kabboul, Murray Krahn
2013· article· en· Medical Decision Making· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
CHANGING HEALTH TECHNOLOGY ASSESSMENT PARADIGMS?
Don Husereau, Chris Henshall, Laura Sampietro-Colom, Sarah Thomas
2016· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
39
citations
affunlabeled
Fragility indices for only sufficiently likely modifications
Benjamin R. Baer, Mario Gaudino, Mary E. Charlson, Stephen E. Fremes, Martin T. Wells
2021· article· en· Proceedings of the National Academy of Sciences· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
39
citations
affaboutunlabeled
A Time-Trend Economic Analysis of Cancer Drug Trials
Sonya Cressman, George P. Browman, Jeffrey S. Hoch, Laurel Kovacic, Stuart Peacock
2015· article· en· The Oncologist· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
39
citations
affunlabeled
Linear Fitted-Q Iteration with Multiple Reward Functions
Daniel J. Lizotte, Michael Bowling, Susan A. Murphy
2013· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
39
citations
fundno affunlabeled
Comparison of SF-6D and EQ-5D Scores in Patients With Breast Cancer
Mahmood Yousefi, Safa Najafi, Shahram Ghaffari, Alireza Mahboub–Ahari, Hossein Ghaderi
2016· article· en· Iranian Red Crescent Medical Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
39
citations

How this was built: Screen · Findings · About