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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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Simulation-Based Education in Healthcare
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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.

2,300 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.
2,300 works in the cohort · of 4,299,418page 1 of 46

Labels cover 5 of 2,300 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 2,300 of 2,300 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
Training and simulation for patient safety
Raj Aggarwal, Oliver Mytton, Miliard Derbrew, David Hananel, Matthew Heydenburg, S. Barry Issenberg +6 more
2010· article· en· BMJ Quality & Safety· Medicine
machine prediction:candidate · noneconsensus · none
722
citations
affunlabeled
More Than One Way to Debrief
Taylor Sawyer, Walter Eppich, Marisa Brett-Fleegler, Vincent Grant, Adam Cheng
2016· review· en· Simulation in Healthcare The Journal of the Society for Simulation in Healthcare· Medicine
machine prediction:candidate · noneconsensus · none
677
citations
affunlabeled
Value of Debriefing during Simulated Crisis Management
Georges L. Savoldelli, Viren N. Naik, Jason Park, Hwan S. Joo, Roger Chow, Stanley J. Hamstra
2006· article· en· Anesthesiology· Medicine
machine prediction:candidate · noneconsensus · none
424
citations
afffundunlabeled
Reporting Guidelines for Health Care Simulation Research
Adam Cheng, David Kessler, Ralph MacKinnon, Todd P. Chang, Vinay Nadkarni, Elizabeth A. Hunt +8 more
2016· article· en· Simulation in Healthcare The Journal of the Society for Simulation in Healthcare· Medicine
machine prediction:candidate · metaresearchconsensus · metaresearch
390
citations
affunlabeled
Debriefing Assessment for Simulation in Healthcare
Marisa Brett-Fleegler, Jenny W. Rudolph, Walter Eppich, Michael C. Monuteaux, Eric W. Fleegler, Adam Cheng +1 more
2012· article· en· Simulation in Healthcare The Journal of the Society for Simulation in Healthcare· Medicine
machine prediction:candidate · noneconsensus · none
367
citations
affunlabeled
Designing and Conducting Simulation-Based Research
Adam Cheng, Marc Auerbach, Elizabeth A. Hunt, Todd P. Chang, Martin Pusic, Vinay Nadkarni +1 more
2014· review· en· PEDIATRICS· Medicine
machine prediction:candidate · metaresearchconsensus · none
233
citations
affunlabeled
Faculty Development for Simulation Programs
Adam Cheng, Vincent Grant, Peter Dieckmann, Sonal Arora, Traci Robinson, Walter Eppich
2015· article· en· Simulation in Healthcare The Journal of the Society for Simulation in Healthcare· Medicine
machine prediction:candidate · noneconsensus · none
202
citations

How this was built: Screen · Findings · About