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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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Meta-analysis and systematic reviews
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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.

4,076 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.
4,076 works in the cohort · of 4,299,418page 35 of 82

Labels cover 359 of 4,076 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 4,076 of 4,076 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.

afffundno abstractunlabeled
Environmental sciences benefit from robust evidence irrespective of speed
Dominique G. Roche, Joseph Bennett, Jennifer F. Provencher, Trina Rytwinski, Neal Haddaway, Steven J. Cooke
2019· article· en· The Science of The Total Environment· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
15
citations
affgemma · metaresearch+metaepi_broadgpt · metaresearch+metaepi_narrow+metaepi_broadmodels split
Quantifying the impact of immortal time bias: empirical evidence from meta-analyses
Min Seo Kim, Dong Keon Yon, Seung Won Lee, Masoud Rahmati, Marco Solmi, André F. Carvalho +4 more
2025· article· en· Journal of the Royal Society of Medicine· Decision Sciences
machine prediction:candidate · metaresearch+metaepi_broadconsensus · metaresearch
15
citations
afffundgemma · metaresearch+metaepi_broadgpt · metaresearch+metaepi_narrow+metaepi_broadmodels split
How should we evaluate the risk of bias of physical therapy trials?: a psychometric and meta-epidemiological approach towards developing guidelines for the design, conduct, and reporting of RCTs in Physical Therapy (PT) area: a study protocol
Susan Armijo‐Olivo, Jorge Fuentes, Todd Rogers, Lisa Hartling, Humam Saltaji, Greta G. Cummings
2013· article· en· Systematic Reviews· Decision Sciences
machine prediction:candidate · metaresearch+metaepi_broadconsensus · metaresearch
15
citations
affgemma · metaresearchgpt · no categorymodels split
Rapid review methods series: Guidance on the use of supportive software
Lisa Affengruber, Barbara Nußbaumer-Streit, Candyce Hamel, Miriam Van der Maten, James Thomas, Chris Mavergames +2 more
2024· article· en· BMJ evidence-based medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
15
citations
affno abstractunlabeled
Rating the quality of evidence is by necessity a matter of judgment
M. Hassan Murad, Reem A. Mustafa, Rebecca L. Morgan, Shahnaz Sultan, Yngve Falck–Ytter, Philipp Dahm
2016· letter· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
14
citations
afffundunlabeled
How do I interpret a p value?
Sheila F. O’Brien, Lori Osmond, Qilong Yi
2015· article· en· Transfusion· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
14
citations

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