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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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Healthcare cost, quality, practices
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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.

1,204 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.
1,204 works in the cohort · of 4,299,418page 8 of 25

Labels cover 31 of 1,204 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 1,204 of 1,204 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.

aboutno affunlabeled
Zu viele Herzkatheteruntersuchungen in Deutschland?
Martin Gottwik, Uwe Zeymer, Sid J. Schneider, Jochen Senges
2003· article· de· DMW - Deutsche Medizinische Wochenschrift· Health Professions
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Too Much Cancer Care?
Moriah Ellen, Saritte Perlman, Ruth Shach
2020· article· en· Cancer Nursing· Health Professions
machine prediction:candidate · noneconsensus · none
6
citations
affvenueno abstractunlabeled
Choosing Wisely
Douglas Urness, Naomi J. Parker, Mark Rapoport, T. Christopher Wilkes
2016· article· en· The Canadian Journal of Psychiatry· Health Professions
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Choosing wisely in burn care
Alan D. Rogers, André Carlos Kajdacsy-Balla Amaral, Robert Cartotto, Anwar Khatib, Robert Fowler, Sarvesh Logsetty +6 more
2021· article· en· Burns· Health Professions
machine prediction:candidate · metaresearchconsensus · none
6
citations
afffundaboutunlabeled
Low value cardiac testing and Choosing Wisely
R. Sacha Bhatia, Wendy Levinson, Douglas S. Lee
2014· letter· en· BMJ Quality & Safety· Health Professions
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Making Wise Choices in Health Provision
Moriah Ellen, Einav Horowitz
2017· article· en· Journal of Nursing Care Quality· Health Professions
machine prediction:candidate · noneconsensus · none
6
citations
venueaboutno affunlabeled
Choosing Wisely Canada recommendations
Lenora Brace
2021· article· en· Canadian Family Physician· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
6
citations

How this was built: Screen · Findings · About