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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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Pharmacovigilance and Adverse Drug Reactions
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.

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.

622 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.
622 works in the cohort · of 4,299,418page 10 of 13

Labels cover 2 of 622 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 622 of 622 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.

venueno affunlabeled
Correction
2019· article· en· Canadian Society of Forensic Science Journal· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Reply to ‘Indication bias or protopathic bias?’
Sharon Daniel, Gideon Koren, Eitan Lunenfeld, Amalia Levy
2015· letter· en· British Journal of Clinical Pharmacology· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
PM_035584_B_Oudenaarde
2009· other· nl· Zenodo (CERN European Organization for Nuclear Research)· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/b978-0-7020-7605-3.00017-3
2000· book-chapter· en· Time to knit· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Regulatory Authority in Pharmacovigilance
Yash Raju Kolte
2023· article· en· International Journal for Research in Applied Science and Engineering Technology· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1553-3212(05)71190-0
2000· article· en· Time to knit· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Introduction
Thamizharasan Sampath, Sandhiya Thamizharasan, Bharanidharan Indrakumar, Monisha Saravanan
2024· book-chapter· en· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1553-3212(11)70194-7
2000· article· en· Time to knit· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affvenueaboutunlabeled
Corrections
Carole Légaré, Christoper Turner, Anges Klein
2004· article· en· Canadian Medical Association Journal· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About