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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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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.

3,958 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.
3,958 works in the cohort · of 4,299,418page 58 of 80

Labels cover 10 of 3,958 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 3,958 of 3,958 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
Patient et citoyen
Nortin Hadler, Fernand Turcotte
2014· book· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
La curiosité est‑elle un don ?
Sylvie Gendreau
2017· article· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
VINCENT MÉNARD
2008· article· en· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
VARIANTS GÉNÉTIQUES
2015· article· en· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Les infirmieres
Marie-Claude Bouchard
2001· book· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
ultime fleur
Jean. Chapuis
2011· book· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Enjeux éthiques de l’IA en santé - Fiche 4
Joé T. Martineau, Frédérique Romy Godin, Janine Badr, Alexandre Castonguay, Martin Cousineau, Philippe Després +4 more
2022· report· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Le Burnout des médecins généralistes
Pascale Jonckheer, Sabine Stordeur, Guy Lebeer, Michel Roland, Jan De Schampheleire, Marianne De Troyer +4 more
2011· book· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Urgence et espoir
Pierre Boulanger
2005· book-chapter· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Abécédaire de l’IA
Abdoulaye Anne, Elisa Gagnon, Esli Osmanlliu, Esma Aïmeur, Florent Michelot, Florie Brangé +9 more
2024· report· fr· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About