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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.

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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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Statistical Methods in Clinical Trials
Retraction
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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.

1,557 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.
1,557 works in the cohort · of 4,299,418page 20 of 32

Labels cover 46 of 1,557 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 1,557 of 1,557 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
Multiple Comparison Procedures
H. J. Keselman, Burt Holland, Robert A. Cribbie
2005· other· en· Encyclopedia of Statistics in Behavioral Science· Mathematics
machine prediction:candidate · noneconsensus · none
2
citations
affgemma · metaresearch+metaepi_broadgpt · metaresearch+metaepi_narrow+metaepi_broadmodels split
Justification, margin values, and analysis populations for oncologic noninferiority and equivalence trials: a meta-epidemiological study
T. Kleber, Alexander D. Sherry, Andrew Arifin, Gabrielle S. Kupferman, Ramez Kouzy, Joseph Abi Jaoude +7 more
2024· article· en· JNCI Journal of the National Cancer Institute· Mathematics
machine prediction:candidate · metaresearch+metaepi_narrow+metaepi_broadconsensus · metaresearch
2
citations
affgemma · no categorygpt · metaresearchmodels split
Some issues for the evaluation of noninferiority trials
Xuanqian Xie, Myra Wang, Vivian Ng, Nancy Sikich
2018· article· en· Journal of Comparative Effectiveness Research· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
2
citations
afffundunlabeled
Letter to the Editor
Beilei Wu, Alexander R. de Leon
2013· letter· en· Biometrical Journal· Mathematics
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
aboutno affunlabeled
Bias busters: using the right risk-of-bias tools
Madelon van Wely, Julie M. Hastings, Basil C. Tarlatzis, Rui Wang
2025· article· en· Human Reproduction Update· Mathematics
machine prediction:candidate · metaresearchconsensus · metaresearch
2
citations
affunlabeled
Dynamic Treatment Regimes
Erica E. M. Moodie, David A. Stephens
2017· letter· en· Wiley StatsRef: Statistics Reference Online· Mathematics
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Composite confusion
David Massel
2004· letter· en· Journal of the American College of Cardiology· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
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