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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.

affaffiliation
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venuejournal
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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 39 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.

affunlabeled
Two alternatives versus the standard Grading of Recommendations Assessment, Development and Evaluation (GRADE) summary of findings (SoF) tables to improve understanding in the presentation of systematic review results: a three-arm, randomised, controlled, non-inferiority trial
Juan José Yepes-Núñez, Rebecca L. Morgan, Lawrence Mbuagbaw, Alonso Carrasco‐Labra, Stephanie Chang, Susanne Hempel +4 more
2018· review· en· BMJ Open· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
11
citations
venueno affunlabeled
Not all systematic reviews are created equal
Janine Farragher, Samantha Seaton, Katherine E. Stewart, Clarice Ribeiro Soares Araújo
2018· editorial· en· Canadian Journal of Occupational Therapy· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
11
citations
affno abstractunlabeled
Principles of Systematic Reviews and Meta-analyses
Rebecca L. Morgan, Iván D. Flórez
2021· article· en· Methods in molecular biology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
11
citations
affunlabeled
A Brief Guide to Evaluate Replications
Etienne P. LeBel, Wolf Vanpaemel, Irene Y. Cheung, Lorne Campbell
2018· preprint· en· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
11
citations
afffundvenueaboutunlabeled
Changing the culture is a marathon not a sprint
Jenna Dixon, Susan J. Elliott
2019· article· en· Allergy Asthma and Clinical Immunology· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affgemma · metaresearchgpt · metaresearch+metaepi_broadmodels split
Systematic Reviews of Systematic Quantitative, Qualitative, and Mixed Studies Reviews in Healthcare Research: How to Assess the Methodological Quality of Included Reviews?
Geneviève Rouleau, Quan Nha Hong, Navdeep Kaur, Marie‐Pierre Gagnon, José Côté, Julien Bouix‐Picasso +1 more
2021· article· en· Journal of Mixed Methods Research· Decision Sciences
machine prediction:candidate · metaresearch+metaepi_broadconsensus · metaresearch
10
citations

How this was built: Screen · Findings · About