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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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Advances in Oncology and Radiotherapy
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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.

1,531 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.
1,531 works in the cohort · of 4,299,418page 7 of 31

Labels cover 8 of 1,531 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 1,531 of 1,531 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.

affaboutunlabeled
Burnout among Canadian oncologists and oncology residents.
Thao Phuong Nguyen, Vincent C. Tam, Renee Lester, Juan Carlos Ruiz, Antoine Bouchard‐Fortier, Cynthia Card +4 more
2014· article· en· Journal of Clinical Oncology· Medicine
machine prediction:candidate · noneconsensus · none
6
citations
venueno affunlabeled
Medical Aspects Taken for Granted
Mohammad Ahad Qayyum, Ahmad Ayaz Sabri, Fawad Aslam
2020· article· en· McGill Journal of Medicine· Medicine
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Are You a Researching Radiation Therapist?
Winnie Li
2014· editorial· en· Journal of medical imaging and radiation sciences· Medicine
machine prediction:candidate · noneconsensus · none
6
citations
afffundaboutno abstractunlabeled
Staffing Levels and Workload for Radiation Therapists in Canada
Alison Giddings, Luminiţa Nica, John French, Carol‐Anne Davis, Marcia Smoke, Amanda Bolderston
2019· article· en· Journal of medical imaging and radiation sciences· Medicine
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Web based pathology assessment in RTOG 98-04
Wendy A. Woodward, Nour Sneige, Kathryn Winter, Henry M. Kuerer, Clifford A. Hudis, Eileen Rakovitch +8 more
2014· article· en· Journal of Clinical Pathology· Medicine
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Pathology Training for Cancer Diagnosis in Africa
Daniel Seymour, Katy Graef, Yawale Iliyasu, Mohenou Isidore Jean-Marie Diomande, Samuel Jaquet, Melissa Kelly +2 more
2021· article· en· American Journal of Clinical Pathology· Medicine
machine prediction:candidate · noneconsensus · none
5
citations
afffundaboutunlabeled
Radiation oncology in Canada
Meredith Giuliani, Mary Gospodarowicz
2017· review· en· Japanese Journal of Clinical Oncology· Medicine
machine prediction:candidate · noneconsensus · none
5
citations
affaboutunlabeled
Improving radiotherapy utilization rates in Ontario.
Michelle Ang, Eric Gutierrez, Nicoda Foster, Lisa Favell, Padraig Warde
2014· article· en· Journal of Clinical Oncology· Medicine
machine prediction:candidate · noneconsensus · none
5
citations
affvenueunlabeled
Cancer: it's time to change the sign
James Downar
2010· article· en· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
5
citations

How this was built: Screen · Findings · About