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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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Surgical Simulation and Training
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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.

2,557 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.
2,557 works in the cohort · of 4,299,418page 3 of 52

Labels cover 3 of 2,557 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 2,557 of 2,557 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
Assessing Technical Competence in Surgical Trainees
Péter Szász, Marisa Louridas, Kenneth A. Harris, Rajesh Aggarwal, Teodor Grantcharov
2014· review· en· Annals of Surgery· Medicine
machine prediction:candidate · noneconsensus · none
108
citations
aboutno affunlabeled
Dissection: A positive experience
M.A. Mc Garvey, Thomas B. Farrell, Ronán Conroy, Shivanthi Kandiah, W. S. Monkhouse
2001· article· en· Clinical Anatomy· Medicine
machine prediction:candidate · noneconsensus · none
108
citations
affaboutunlabeled
Virtual Surgical Planning: The Pearls and Pitfalls
Johnny Ionut Efanov, Andrée-Anne Roy, Ke Huang, Daniel E. Borsuk
2018· article· en· Plastic & Reconstructive Surgery Global Open· Medicine
machine prediction:candidate · noneconsensus · none
107
citations
affaboutunlabeled
Transition to Surgical Residency
Rebecca M. Minter, Keith D. Amos, Michael L. Bentz, Patrice Gabler Blair, Christopher P. Brandt, Jonathan D’Cunha +11 more
2015· article· en· Academic Medicine· Medicine
machine prediction:candidate · noneconsensus · none
105
citations
affno abstractunlabeled
Using operative outcome to assess technical skill
David Szalay, Helen MacRae, Glenn Regehr, Richard K. Reznick
2000· article· en· The American Journal of Surgery· Medicine
machine prediction:candidate · noneconsensus · none
98
citations
afffundno abstractunlabeled
Surgical Skill Assessment Using Motion Quality and Smoothness
Ahmad Ghasemloonia, Yaser Maddahi, Kourosh Zareinia, Sanju Lama, Joseph C. Dort, Garnette R. Sutherland
2016· article· en· Journal of surgical education· Medicine
machine prediction:candidate · noneconsensus · none
98
citations
afffundaboutunlabeled
Teaching Technical Skills
Kyle R. Wanzel, Edward D. Matsumoto, Stanley J. Hamstra, Dimitri J. Anastakis
2002· article· en· Plastic & Reconstructive Surgery· Medicine
machine prediction:candidate · noneconsensus · none
98
citations

How this was built: Screen · Findings · About