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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 24 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
A clinician’s guide to network meta-analysis
Mark Phillips, David Steel, Charles C. Wykoff, Jason W. Busse, Raveendhara R. Bannuru, Lehana Thabane +11 more
2022· editorial· en· Eye· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
34
citations
affgemma · no categorygpt · no categorymodels agree
An Introduction to Individual Participant Data Meta-analysis
Areti Angeliki Veroniki, Georgios Seitidis, Georgios Tsivgoulis, Aristeidis H. Katsanos, Dimitris Mavridis
2023· review· en· Neurology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
33
citations
affgemma · no categorygpt · no categorymodels agree
Bayesian Variance Estimation for Meta-Analysis
Piers Steel, John D. Kammeyer‐Mueller
2007· article· en· Organizational Research Methods· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
33
citations
affunlabeled
Automation tools to support undertaking scoping reviews
Hanan Khalil, Danielle Pollock, Patricia McInerney, Catrin Evans, Érica Brandão de Moraes, Christina Godfrey +8 more
2024· article· en· Research Synthesis Methods· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
33
citations
affno abstractunlabeled
CONSORT 2010
Kenneth F. Schulz, David Moher, Douglas G. Altman
2010· letter· en· The Lancet· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
32
citations
afffundunlabeled
Ongoing monitoring of data clustering in multicenter studies
Lauren Guthrie, Emily Oken, Jonathan A C Sterne, Matthew W. Gillman, Rita Patel, Konstantin Vilchuck +3 more
2012· article· en· BMC Medical Research Methodology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
32
citations
affno abstractunlabeled
Reporting Random Controlled Trials of Herbal Medicines
Joel Gagnier, Heather Boon, Paula A. Rochon, Joanne Barnes, David Moher, Claire Bombardier
2006· article· en· EXPLORE· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
31
citations

How this was built: Screen · Findings · About