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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
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.

2,679 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.
2,679 works in the cohort · of 4,299,418page 8 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.

afffundgemma · metaresearchgpt · metaresearchmodels split
Optimising the process for conducting scoping reviews
Colleen Pawliuk, Helen Brown, Kim Widger, Tammie Dewan, Anne‐Mette Hermansen, Marie-Claude Grégoire +2 more
2020· review· en· BMJ evidence-based medicine· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
28
citations
aboutno affunlabeled
A Tool for Reviewers
Georges Bordage, Addeane S. Caelleigh
2001· article· en· Academic Medicine· Health Professions
machine prediction:candidate · metaresearchconsensus · none
27
citations
afffundvenueaboutunlabeled
Acknowledging Librarians’ Contributions to Systematic Review Searching
Robin Desmeules, Sandy Campbell, Marlene Dorgan
2016· article· en· Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
27
citations
affunlabeled
Personal Digital Educators
James J. Cimino, Suzanne Bakken
2005· article· en· New England Journal of Medicine· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
27
citations
affunlabeled
Thoughtful use of diagnostic testing
Tom Bartol
2015· article· en· The Nurse Practitioner· Health Professions
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
Revisiting the journal club
Marie‐Thérèse Cave, D. Jean Clandinin
2007· article· en· Medical Teacher· Health Professions
machine prediction:candidate · metaresearchconsensus · none
26
citations
aboutno affunlabeled
Editorial
David R. Thompson
2003· editorial· en· Journal of Advanced Nursing· Health Professions
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
A CALL FOR KNOWLEDGE TRANSLATION IN NURSING RESEARCH
Elisiane Lorenzini, Davina Banner, Katrina Plamondon, Nelly D. Oelke
2019· article· en· Texto & Contexto - Enfermagem· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
25
citations
aboutno affunlabeled
Evidence-Based Mental Health
John Geddes, Shirley Reynolds, David L. Streiner, Péter Szatmári
2002· article· en· Evidence-Based Mental Health· Health Professions
machine prediction:candidate · noneconsensus · none
25
citations

How this was built: Screen · Findings · About