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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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Patient Satisfaction in Healthcare
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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.

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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.

1,023 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.
1,023 works in the cohort · of 4,299,418page 7 of 21

Labels cover 8 of 1,023 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 1,023 of 1,023 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
Components of coordinated care
Rhonda Cockerill, Susan Jaglal, Louise Lemieux Charles, Larry W. Chambers, Kevin Brazil, Carole Cohen
2006· article· en· Dementia· Health Professions
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Determining clinician satisfaction with telemedicine
Craig Kennedy, Kathy Johnston, Paul Taylor, Ian Murdoch
2003· article· en· Journal of Telemedicine and Telecare· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
affvenueno abstractunlabeled
Vision to improve: quality improvement in ophthalmology
Jingyi Ma, Brian M. Wong, Jonathan A. Micieli, Jennifer Calafati, Stephanie A.W. Low, Sherif El-Defrawy +1 more
2019· review· en· Canadian Journal of Ophthalmology· Health Professions
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Leveraging Data to Transform Nursing Care
Lianne Jeffs, Vera Nincic, Peggy White, Laureen Hayes, Joyce Lo
2014· article· en· Journal of Nursing Care Quality· Health Professions
machine prediction:candidate · metaresearchconsensus · none
11
citations
affunlabeled
Establishing a global quality of care benchmark report
Fanny Sampurno, Justin Cally, Jacinta Opie, Ashwini Kannan, Jeremy Millar, Antonio Finelli +20 more
2021· article· en· Health Informatics Journal· Health Professions
machine prediction:candidate · metaresearchconsensus · none
11
citations
affno abstractunlabeled
Development of a Case Management Quality Questionnaire
Heather D. Hadjistavropoulos, Mark Sagan, Cecily Bierlein, Karen Lawson
2003· article· en· Care management journals· Health Professions
machine prediction:candidate · noneconsensus · none
10
citations

How this was built: Screen · Findings · About