MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Healthcare Operations and Scheduling Optimization
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

988 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
988 works in the cohort · of 4,299,418page 3 of 20

Labels cover 1 of 988 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 988 of 988 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
Delays to surgery in non-small-cell lung cancer.
Moïshe Liberman, D. Liberman, John S. Sampalis, David S. Mulder
2006· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
51
citations
affaboutunlabeled
Watching Your Wait
Sara A. Kreindler
2008· review· en· Quality Management in Health Care· Health Professions
machine prediction:candidate · noneconsensus · none
45
citations
affaboutunlabeled
A simulation model for perioperative process improvement
Solmaz Azari-Rad, Alanna L. Yontef, Dionne M. Aleman, David R. Urbach
2014· article· en· Operations Research for Health Care· Health Professions
machine prediction:candidate · noneconsensus · none
45
citations
affno abstractunlabeled
Capacity planning for cardiac catheterization: A case study
Diwakar Gupta, Madhu K. Natarajan, Amiram Gafni, Lei Wang, Don Shilton, Douglas H. Holder +1 more
2006· article· en· Health Policy· Health Professions
machine prediction:candidate · noneconsensus · none
42
citations
affunlabeled
Hospital capacity management based on the queueing theory
Otávio Neves da Silva Bittencourt, Vedat Verter, Morty Yalovsky
2018· article· en· International Journal of Productivity and Performance Management· Health Professions
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
Managing Patient Admissions in a Neurology Ward
Saied Samiedaluie, Beste Küçükyazicı, Vedat Verter, Dan Zhang
2017· article· en· Operations Research· Health Professions
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
Impact of Surgical Waiting-List Times on Scoliosis Surgery
Firoz Miyanji, Peter O. Newton, Amer F. Samdani, Suken A. Shah, Ranjit Varghese, Christopher W. Reilly +1 more
2014· article· en· Spine· Health Professions
machine prediction:candidate · noneconsensus · none
38
citations
affunlabeled
Observations on Surgical Demand Time Series
Ian Moore, David P. Strum, Luís G. Vargas, David J. Thomson
2008· article· en· Anesthesiology· Health Professions
machine prediction:candidate · noneconsensus · none
37
citations

How this was built: Screen · Findings · About