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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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Medical Coding and Health Information
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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
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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.

679 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.
679 works in the cohort · of 4,299,418page 5 of 14

Labels cover 6 of 679 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 679 of 679 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
What is SNOMED CT® and Why Should the ISSHP Care?
Kiran Angelina Massey, J. Mark Ansermino, Peter von Dadelszen, Tara Morris, Robert M. Liston, Laura A. Magee
2009· article· en· Hypertension in Pregnancy· Health Professions
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Data entry and data accuracy
Hartzell V. Schaff, Morgan L. Brown, Judy R. Lenoch
2010· letter· en· Journal of Thoracic and Cardiovascular Surgery· Health Professions
machine prediction:candidate · metaresearchconsensus · none
5
citations
affno abstractunlabeled
Health Information Science
Xiaoxia Yin, Kendall Ho, Daniel Zeng, Uwe Aickelin, Rui Zhou, Hua Wang
2015· book· en· Lecture notes in computer science· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
The missing data dilemma.
Mae Squires, Ann E. Tourangeau
2009· article· fr· PubMed· Health Professions
machine prediction:candidate · metaresearchconsensus · none
4
citations
affunlabeled
Multivariable Risk Prediction Models
Gary S. Collins, Yannick Le Manach
2013· letter· en· Anesthesiology· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Everest
Justin Fyfe, Duane Bender, H. Keith Edwards
2012· article· en· ACM SIGHIT Record· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Epidemiology and Health Administrative Data
Eric I. Benchimol
2014· letter· en· Inflammatory Bowel Diseases· Health Professions
machine prediction:candidate · noneconsensus · none
4
citations
aboutno affunlabeled
The ABCs of ICD
Richard Salcido
2015· editorial· en· Advances in Skin & Wound Care· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
aboutno affno abstractunlabeled
Measuring Health: Lessons for Ontario
2023· article· en· Project Muse (Johns Hopkins University)· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Engineering data equity: the LISTEN principles
Colin J. Carlson, Mónica Granados, Alexandra Phelan, Timothée Poisot
2024· preprint· en· SSRN Electronic Journal· Health Professions
machine prediction:candidate · open_scienceconsensus · none
3
citations
affno abstractunlabeled
Countries should strengthen their health information systems
Kathleen Strong, Amanuel Alemu Abajobir, Frances E. Aboud, Ambrose Agweyu, Sk Masum Billah, Maureen M. Black +11 more
2025· letter· en· BMJ· Health Professions
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About