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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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Electronic Health Records Systems
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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
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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.

2,603 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.
2,603 works in the cohort · of 4,299,418page 16 of 53

Labels cover 6 of 2,603 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 2,603 of 2,603 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.

afffundaboutunlabeled
Replacing an Inpatient Electronic Medical Record
Guy Paré, M-P Moreault, Anne Lemay, Luc Valiquette, Alan Barkun, C. Sicotte
2008· article· en· Methods of Information in Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
17
citations
affaboutunlabeled
Seniors' views on the use of electronic health records
Diane Morin, André Tourigny, Daniel Pelletier, Line Robichaud, Luc Mathieu, Aline V zina +2 more
2005· article· en· Journal of Innovation in Health Informatics· Health Professions
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Past and Next 10 Years of Medical Informatics
Frank Ückert, Elske Ammenwerth, Carl Dujat, Andrew Grant, Reinhold Haux, Andreas Hein +14 more
2014· article· en· Journal of Medical Systems· Health Professions
machine prediction:candidate · metaresearchconsensus · none
17
citations
affunlabeled
Nursing Information Systems Requirements
Mehrdad Farzandipour, Zahra Meidani, Hossein Riazi, Monireh Sadeqi Jabali
2016· article· en· CIN Computers Informatics Nursing· Health Professions
machine prediction:candidate · noneconsensus · none
16
citations

How this was built: Screen · Findings · About