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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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Medication Adherence and Compliance
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.

1,323 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.
1,323 works in the cohort · of 4,299,418page 24 of 27

Labels cover 9 of 1,323 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 1,323 of 1,323 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
Redefining compliance education
Lorna June Cochrane
2003· dissertation· en· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Talking about…
G. BRUTTOMESSO, N. MIGLINO
2006· article· en· Journal of Thrombosis and Haemostasis· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
The Burden of Self-Dialysis: Opinion From Patient's Side
Giorgio Soragna, Elisabetta Mezza, Francesca Bermond, Patrizia Longo, Marinella Talaia, Valeria Bianchi +2 more
2003· article· en· Hemodialysis International· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Quarterly report. 2020: Apr. 1 to Jun. 30.
2020· article· en· State Elections Enforcement Commission (State of Connecticut)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
RxA Survey of Members: 70% Plan to Prescribe
Kathie Lynas
2007· article· fr· Canadian Pharmacists Journal / Revue des Pharmaciens du Canada· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0029-7437(07)70408-1
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
5PSQ-134 Unit dose in a cyberattack scenario
Alberto M. Soares, AM Simões, Paulo Victor Viana dos Santos, PAULA RENATA DO NASCIMENTO ALMEIDA, M Rodrigues, Andressa Griebler Gusmão +1 more
2023· article· en· Section 5: Patient safety and quality assurance· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affvenueno abstractunlabeled
Improving Adherence to Medications
Susan Pierce
2007· article· en· Canadian Pharmacists Journal / Revue des Pharmaciens du Canada· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
‘A Fool for a Patient’
Ben Wedro
2012· article· en· Emergency Medicine News· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
PEI PharmaCheck program
Kathie Lynas
2013· article· fr· Canadian Pharmacists Journal / Revue des Pharmaciens du Canada· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Research update.
RL Collins-Nakai
2001· article· en· PubMed· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
Corrigendum and Editorial Warning Regarding Use of the MMAS Scale (The Ready to Reduce Risk (3R) Study for a Group Educational Intervention With Telephone and Text Messaging Support to Improve Medication Adherence for the Primary Prevention of Cardiovascular Disease: Protocol for a Randomized Controlled Trial)
Jo Byrne, Helen Dallosso, Stephen Rogers, Laura J. Gray, Ghazala Waheed, Prashanth Patel +4 more
2019· erratum· en· JMIR Research Protocols· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About