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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 13 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.

affno abstractunlabeled
Network Meta-Analysis
Jennifer Watt, Cinzia Del Giovane
2021· article· en· Methods in molecular biology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
89
citations
affno abstractunlabeled
GRADE guidelines 33: Addressing imprecision in a network meta-analysis
Romina Brignardello‐Petersen, Gordon Guyatt, Reem A. Mustafa, Derek K. Chu, Monica Hultcrantz, Holger J. Schünemann +1 more
2021· article· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
89
citations
affunlabeled
GRADE system: new paradigm
Luigi Terracciano, Jan Brożek, Enrico Compalati, Holger J. Schünemann
2010· review· en· Current Opinion in Allergy and Clinical Immunology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
88
citations
afffundgemma · metaresearchgpt · metaresearchmodels split
Reproducibility of clinical research in critical care: a scoping review
Daniel J. Niven, Thomas Jared McCormick, Sharon E. Straus, Brenda R. Hemmelgarn, Lianne Jeffs, Tavish Barnes +1 more
2018· review· en· BMC Medicine· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
87
citations
affunlabeled
Evaluation of the JBI scoping reviews methodology by current users
Hanan Khalil, Marsha Bennett, Christina Godfrey, Patricia McInerney, Zac Munn, Micah D.J. Peters
2019· article· en· International Journal of Evidence-Based Healthcare· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
86
citations
affno abstractunlabeled
Comparing samples—part II
Martin Krzywinski, Naomi Altman
2014· article· en· Nature Methods· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
85
citations
afffundno abstractgemma · metaresearch+metaepi_broadgpt · metaresearch+metaepi_broadmodels agree
Flaws in the application and interpretation of statistical analyses in systematic reviews of therapeutic interventions were common: a cross-sectional analysis
Matthew J. Page, Douglas G. Altman, Joanne E. McKenzie, Larissa Shamseer, Nadera Ahmadzai, Dianna Wolfe +4 more
2017· article· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
85
citations
affunlabeled
A primer on systematic reviews in toxicology
Sebastian Hoffmann, Rob B.M. de Vries, Martin L. Stephens, Nancy B. Beck, Hubert Dirven, John R. Fowle +8 more
2017· review· es· Archives of Toxicology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
84
citations
affunlabeled
How to Conduct a Bayesian Network Meta-Analysis
Dapeng Hu, Annette M. O’Connor, Chong Wang, Jan M. Sargeant, Charlotte B. Winder
2020· article· en· Frontiers in Veterinary Science· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
83
citations
affgemma · metaresearchgpt · metaresearchmodels split
Updating standards for reporting diagnostic accuracy: the development of STARD 2015
Daniël A. Korevaar, Jérémie F. Cohen, Johannes B. Reitsma, David E. Bruns, Constantine Gatsonis, Paul Glasziou +6 more
2016· article· en· Research Integrity and Peer Review· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
82
citations

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