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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
Evidence
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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 55 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
Core competencies for ethics experts in health technology assessment
Pietro Refolo, Kenneth Bond, Bart Bloemen, Ilona Autti‐Rämö, Bjørn Hofmann, Claudia Mischke +8 more
2020· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Health Care Prioritization: A Clinician's Duty
Lianne Barnieh, Cam Donaldson, Braden Manns
2014· article· en· Canadian Journal of Kidney Health and Disease· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations
afffundaboutunlabeled
Medicines pricing and reimbursement in Canada
Chris BONNETT, Tania Stafinski, Evelinda Trindade
2022· article· en· Revista Brasileira de Farmácia Hospitalar e Serviços de Saúde· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations
affvenueunlabeled
Improving Drug Trials for Mild to Moderate Alzheimer's Disease
David B. Hogan
2007· review· en· Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
6
citations
aboutno affunlabeled
PD45 Paying For Digital Health: What Evidence Is Needed?
Anita Burrell, Vlad Zah, Zsombor Zrubka, Carl V. Asche
2022· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractgemma · no categorygpt · insufficient_payloadmodels split
Future research and methodological approaches
Joe Pater, Justine Rochon, Mahesh Parmar, Peter J. Selby
2011· article· en· Annals of Oncology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Responses to Comments of Weis
Na Guo, Carlo A. Marra, Fawziah Marra
2010· letter· en· Health and Quality of Life Outcomes· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations
affvenueunlabeled
Measuring frailty in geriatric patients
Kenneth Rockwood
2006· article· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
6
citations

How this was built: Screen · Findings · About