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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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Hemoglobinopathies and Related Disorders
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.

2,029 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.
2,029 works in the cohort · of 4,299,418page 8 of 41

Labels cover 1 of 2,029 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 2,029 of 2,029 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.

affunlabeled
Molecular Epidemiology of Hemoglobinopathies in Cambodia
Thongperm Munkongdee, Jatuporn Tanakulmas, Punnee Butthep, Pranee Winichagoon, Barbara Main, Miriam Yiannakis +4 more
2016· article· en· Hemoglobin· Medicine
machine prediction:candidate · noneconsensus · none
26
citations
affno abstractunlabeled
The efficacy of oral deferiprone in acute iron poisoning
Matitiahu Berkovitch, A. Livne, Gili Lushkov, Michael Segal, Clara Talmor, Yedidia Bentur +2 more
2000· article· en· The American Journal of Emergency Medicine· Medicine
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Serum Erythropoietin in Small for Gestational Age Fetuses
D. Lémery, Joaquín Santolaya, Anne F. Serre, Stephanie Denoix, G Besse, Philippe C. Vanlieferinghen +3 more
2009· article· en· Biology of the Neonate· Medicine
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Artificial intelligence in sickle disease
Ahmed Adel Elsabagh, Mohamed Elhadary, Basel Elsayed, Amgad M. Elshoeibi, Khaled Ferih, Rasha Kaddoura +7 more
2023· review· en· Blood Reviews· Medicine
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Iron Overload and Iron-Chelating Therapy in Hemoglobin E-β Thalassemia
Nancy F. Olivieri, Shanthimala de Silva, Anuja Premawardena, Supriya Sharma, A. M. Viens, Chelsea Maria Taylor +2 more
2000· article· en· ˜The œAmerican journal of pediatric hematology/oncology· Medicine
machine prediction:candidate · noneconsensus · none
23
citations
fundno affunlabeled
Rifaximin for sickle cell disease
Seah H. Lim, Dibyendu Dutta, Judy Moore
2019· letter· en· American Journal of Hematology· Medicine
machine prediction:candidate · noneconsensus · none
22
citations
affvenueunlabeled
Comparing the Effectiveness of Education Versus Digital Cognitive Behavioral Therapy for Adults With Sickle Cell Disease: Protocol for the Cognitive Behavioral Therapy and Real-time Pain Management Intervention for Sickle Cell via Mobile Applications (CaRISMA) Study
Sherif M. Badawy, Kaleab Z. Abebe, Charlotte A Reichman, Grace Checo, Megan Hamm, Jennifer Stinson +9 more
2021· article· en· JMIR Research Protocols· Medicine
machine prediction:candidate · noneconsensus · none
22
citations

How this was built: Screen · Findings · About