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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 Sciences Research and Education
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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
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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.

2,679 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.
2,679 works in the cohort · of 4,299,418page 13 of 54

Labels cover 60 of 2,679 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 2,679 of 2,679 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
Why Using Research Matters
Anna Ehrenberg, Carole A. Estabrooks
2004· article· en· Journal of Wound Ostomy and Continence Nursing· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
14
citations
affaboutunlabeled
IAM
Pierre Pluye, Roland Grad, Carol Repchinsky, Barbara Farrell, Janique Johnson‐Lafleur, Tara Bambrick +1 more
2009· book-chapter· en· IGI Global eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Do patients understand risk?
Ashish Mahajan
2007· article· en· The Lancet· Health Professions
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Documentation and Record Keeping
Susan Pirie
2011· article· en· Journal of Perioperative Practice· Health Professions
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Cognitive task analysis
Ping Li
2005· article· en· OCLC Systems & Services· Health Professions
machine prediction:candidate · noneconsensus · none
13
citations
afffundno abstractunlabeled
Introducing information literacy into anesthesia curricula
Lisa Demczuk, Tania Gottschalk, Judith Littleford
2009· review· en· Canadian Journal of Anesthesia/Journal canadien d anesthésie· Health Professions
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
The Role of Intermediaries
Linda Ferguson, Margaret Milner, Erna Snelgrove‐Clarke
2004· review· en· Journal of Wound Ostomy and Continence Nursing· Health Professions
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Considering Evidence-based Practice
Barbara Ritter
2001· article· en· The Nurse Practitioner· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
12
citations
affunlabeled
Evidence‐Based Medicine: Why Bother?
Mohit Bhandari
2009· review· en· Arthroscopy The Journal of Arthroscopic and Related Surgery· Health Professions
machine prediction:candidate · metaresearchconsensus · none
12
citations

How this was built: Screen · Findings · About