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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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Opioid Use Disorder Treatment
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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
fundfunder
venuejournal
aboutaboutness

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

4,016 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.
4,016 works in the cohort · of 4,299,418page 57 of 81

Labels cover 19 of 4,016 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 4,016 of 4,016 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.

affvenueno abstractunlabeled
Frequently asked questions about naloxone: Part 2
Ashley Cid, Alec Patten, Kelly Grindrod, Michael A. Beazely
2021· article· en· Canadian Pharmacists Journal / Revue des Pharmaciens du Canada· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
venueaboutno affunlabeled
More regulation not the answer for opioids
Lauren Vogel
2015· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Tendances canadiennes en matière de mortalité liée aux opioïdes et d’invalidité découlant d’un trouble deconsommation d’opioïdes, à la lumière de l’Étude sur la charge mondiale de morbidité (1990-2014)
Heather Orpana, Justin J. Lang, Maulik Baxi, Jessica Halverson, Nicole Kozloff, Leah E. Cahill +2 more
2018· article· fr· Promotion de la santé et prévention des maladies chroniques au Canada· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
Response to “Opioid warning label”
Jason W. Busse
2017· letter· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Buprenorphine Comes of Age
Howard A. Heit, Douglas Gourlay
2007· article· en· Journal of Addiction Medicine· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affno abstractunlabeled
Canada's decision on reducing illicit drug harm
The Lancet Infectious Diseases
2006· editorial· en· The Lancet Infectious Diseases· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
venueaboutno affunlabeled
Four Days of Mayhem: September 4-8, 2022
el-Guebaly Nady
2022· article· en· The Canadian Journal of Addiction· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affvenueno abstractunlabeled
Frequently asked questions about naloxone: Part 1
Ashley Cid, Alec Patten, Kelly Grindrod, Michael A. Beazely
2021· article· en· Canadian Pharmacists Journal / Revue des Pharmaciens du Canada· Medicine
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About