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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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International Journal of Epidemiology
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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.

614 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.
614 works in the cohort · of 4,299,418page 5 of 13

Labels cover 10 of 614 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 614 of 614 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
Causal diagrams for immortal time bias
Mohammad Alì Mansournia, Maryam Nazemipour, Mahyar Etminan
2021· editorial· en· International Journal of Epidemiology· Mathematics
machine prediction:candidate · metaresearchconsensus · none
45
citations
affaboutunlabeled
Data Resource Profile: 1991 Canadian Census Cohort
Paul A. Peters, M.K.G. Tjepkema, Ruth C. Wilkins, Philippe Finès, Dan L. Crouse, Phil C.W. Chan +1 more
2013· article· en· International Journal of Epidemiology· Social Sciences
machine prediction:candidate · noneconsensus · none
45
citations
fundno affno abstractunlabeled
Data Resource Profile: China National Nutrition Surveys
Yuna He, Wen­hua Zhao, Jian Zhang, Liyun Zhao, Zhenyu Yang, Junsheng Huo +8 more
2019· article· en· International Journal of Epidemiology· Medicine
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
The pseudo-high-risk prevention strategy
Arnaud Chioléro, Gilles Paradis, Fred Paccaud
2015· editorial· en· International Journal of Epidemiology· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
36
citations
affno abstractunlabeled
Commentary: Measuring nutritional status of children
Daniel J. Corsi, Malavika A. Subramanyam, S. V. Subramanian
2011· letter· en· International Journal of Epidemiology· Nursing
machine prediction:candidate · noneconsensus · none
35
citations
afffundaboutno abstractunlabeled
Cohort Profile: The British Columbia Generations Project (BCGP)
Anar Dhalla, Treena McDonald, Richard P. Gallagher, John J. Spinelli, Angela Brooks‐Wilson, Tim K. Lee +5 more
2018· article· en· International Journal of Epidemiology· Social Sciences
machine prediction:candidate · noneconsensus · none
35
citations

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