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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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Multiple Sclerosis Research Studies
Retraction
Abstract
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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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aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

4,686 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
4,686 works in the cohort · of 4,299,418page 66 of 94

Labels cover 13 of 4,686 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 4,686 of 4,686 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.

aboutno affunlabeled
Promises, promises…
Alan J. Thompson
2007· editorial· en· Annals of Neurology· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Fatigue in childhood multiple sclerosis
E. Ann Yeh
2016· letter· en· Developmental Medicine & Child Neurology· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Concussion may not cause multiple sclerosis
José M.A. Wijnands, Helen Tremlett
2017· letter· en· Annals of Neurology· Medicine
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
The road to conception for women with multiple sclerosis
A. Dessa Sadovnick, Maria Criscuoli, Irene M. Yee, Robert Carruthers, Alice Schabas, Penelope Smyth
2021· article· en· Multiple Sclerosis Journal - Experimental Translational and Clinical· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Subclinical atherosclerosis in multiple sclerosis
Ruth Ann Marrie, Ronak Patel, Stephen Allan Schaffer
2024· article· en· Multiple Sclerosis Journal - Experimental Translational and Clinical· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
aboutno affunlabeled
The Multiple Sclerosis Severity Score (MSSS) re-examined: EDSS rank stability in the MSBase dataset increases 5 years after onset of multiple sclerosis
Orla Gray, David Jolley, Jeannette Lechner‐Scott, Cees Zwanikken, María Trojano, François Grand’Maison +5 more
2009· article· en· Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B))· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Inequality in accessing healthcare for people with MS
Jeannette Lechner‐Scott, Susan Agland, Gavin Giovannoni, Chris Hawkes, Michaël Lévy, E. Ann Yeh
2023· editorial· en· Multiple Sclerosis and Related Disorders· Medicine
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About