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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
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.

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 5 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
USE OF VALUE OF INFORMATION IN UK HEALTH TECHNOLOGY ASSESSMENTS
Syed Mohiuddin, Elisabeth Fenwick, Katherine Payne
2014· review· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
13
citations
affaboutunlabeled
ALLOGENEIC STEM CELL TRANSPLANTATION
Philip Jacobs, David Hailey, Robert Turner, Nadine MacLean
2000· review· en· International Journal of Technology Assessment in Health Care· Medicine
machine prediction:candidate · noneconsensus · none
13
citations
aboutno affunlabeled
CHALLENGES, CHOICES, AND CANADA
Jill M. Sanders
2002· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
11
citations
fundno affunlabeled
Good Practices for Health Technology Assessment Guideline Development: A Report of the Health Technology Assessment International, HTAsiaLink, and ISPOR Special Task Force
Siobhan Botwright, Manit Sittimart, Kinanti Khansa Chavarina, Diana Beatriz Bayani, Tracy Merlin, Gavin Surgey +5 more
2024· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
11
citations
affunlabeled
Primary data collection in health technology assessment
Michelle McIsaac, Ron Goeree, James M. Brophy
2007· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · metaresearch
10
citations
affunlabeled
Test–retest reliability of the Cost for Patients Questionnaire
Thomas G. Poder, Lucien P. Coulibaly, Abakar Idriss Hassan, Blanchard Conombo, Maude Laberge
2022· article· en· International Journal of Technology Assessment in Health Care· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
10
citations

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