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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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Implementation Science
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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.

633 results · 1 filter active ·
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20062025
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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.
633 works in the cohort · of 4,299,418page 4 of 13

Labels cover 35 of 633 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 633 of 633 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.

afffundaboutunlabeled
Patterns of research utilization on patient care units
Carole A. Estabrooks, Shannon D. Scott, Janet E. Squires, Bonnie Stevens, Linda O’Brien‐Pallas, Judy Watt‐Watson +7 more
2008· article· en· Implementation Science· Health Professions
machine prediction:candidate · metaresearchconsensus · none
111
citations
afffundaboutgpt · no categorygrok · metaepi_broadopus · no categorymodels split
The effectiveness of knowledge translation interventions for promoting evidence-informed decision-making among nurses in tertiary care: a systematic review and meta-analysis
Jennifer Yost, Rebecca Ganann, David R. Thompson, Fazila Aloweni, Kristine Newman, Afeez Abiola Hazzan +3 more
2015· review· en· Implementation Science· Health Professions
machine prediction:candidate · metaresearchconsensus · none
103
citations
affunlabeled
Reporting standards for guideline-based performance measures
Monika Nothacker, Tim Stokes, Beth Shaw, Patrice Lindsay, Raija Sipilä, Markus Follmann +1 more
2015· review· en· Implementation Science· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
99
citations
afffundaboutunlabeled
Looking inside the black box: a theory-based process evaluation alongside a randomised controlled trial of printed educational materials (the Ontario printed educational message, OPEM) to improve referral and prescribing practices in primary care in Ontario, Canada
Jeremy Grimshaw, Merrick Zwarenstein, Jacqueline Tetroe, Gaston Godin, Ian D. Graham, Louise Lemyre +7 more
2007· article· en· Implementation Science· Health Professions
machine prediction:candidate · noneconsensus · none
88
citations

How this was built: Screen · Findings · About