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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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International Journal of Technology Assessment in Health Care
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Retraction
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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
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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.

589 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.
589 works in the cohort · of 4,299,418page 3 of 12

Labels cover 3 of 589 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 589 of 589 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
ORDER SETS IN HEALTH CARE: A SYSTEMATIC REVIEW OF THEIR EFFECTS
Alvita J. Chan, Julie Chan, Joseph A Cafazzo, Peter G. Rossos, Tim Tripp, Kaveh G Shojania +2 more
2012· review· en· International Journal of Technology Assessment in Health Care· Health Professions
machine prediction:candidate · noneconsensus · none
34
citations
afffundaboutunlabeled
COST-EFFECTIVENESS OF EXERCISE PROGRAMS IN TYPE 2 DIABETES
Doug Coyle, Kathryn Coyle, Glen P. Kenny, Normand G. Boulé, George A. Wells, Michelle Fortier +3 more
2012· article· en· International Journal of Technology Assessment in Health Care· Medicine
machine prediction:candidate · noneconsensus · none
31
citations
affunlabeled
THE GREAT ESCAPE?
Mira Johri, Pascale Lehoux
2003· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
27
citations
affaboutunlabeled
DAILY COST PREDICTION MODEL IN NEONATAL INTENSIVE CARE
John A. F. Zupancic, Douglas K. Richardson, Bernie J. OʼBrien, Barbara Schmidt, Milton C. Weinstein
2003· article· en· International Journal of Technology Assessment in Health Care· Medicine
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Improving ethics analysis in health technology assessment
Katherine Duthie, Kenneth Bond
2011· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
26
citations
affaboutunlabeled
BREAST CANCER: BETTER CARE FOR LESS COST
William K. Evans, B P Will, Jean‐Marie Berthelot, D M Logan, Douglas J. Mirsky, Nancy Kelly
2000· article· en· International Journal of Technology Assessment in Health Care· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
24
citations

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