MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Disaster Response and Management
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.

1,873 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.
1,873 works in the cohort · of 4,299,418page 29 of 38

Labels cover 10 of 1,873 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 1,873 of 1,873 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
DO BURN PATIENTS DIE FROM BURNS?
Caryne Lessard, Marc‐Jacques Dubois, P Deroy, David Bracco, Rédouane Bouali, L Duranceau
2005· article· en· Critical Care Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
How Not to Think in an Emergency
Jason MacLean
2020· article· en· Lex Electronica· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Emergency staff is in danger
Betül Gülalp, Özgür Karcıoğlu
2007· article· en· Critical Care· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1553-3212(07)70287-x
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affunlabeled
A guide for honor society virtual induction
Emily Hopkins, Kathleen C. Spadaro
2022· article· en· Journal of Nursing Education and Practice· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Disaster and Mass Fatalities
Max M. Houck, Frank Crispino, Terry McAdam
2018· book-chapter· en· Elsevier eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Se preparer a la pandemie de grippe A H1N1 2009
P. C. Hebert, Noni MacDonald
2009· article· en· Canadian Medical Association Journal· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
TOWARD EVIDENCE-BASED PRACTICE
Roberta L. Woodgate, Heidi V. Krowchuk
2011· article· en· MCN The American Journal of Maternal/Child Nursing· Health Professions
machine prediction:candidate · metaresearchconsensus · none
0
citations
venueno affno abstractunlabeled
Point of Ignition
Lauren Carter
2013· article· en· Dalhousie review· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
Evaluating nurses’ preparedness in critical incidents
Shafic Abdulkarim, Ammar Saed Aldien, Anudari Zorigtbaatar, Natasha Dupuis, Josée Larocque, Tarek Razek
2024· article· en· Journal of Nursing Education and Practice· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0197-2510(05)70213-8
A J Heightman
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
[no title]
Claude Topping
2000· article· en· Canadian Journal of Emergency Medicine· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affno abstractunlabeled
SARS: The Toronto Experience
Bruce Farr
2012· article· en· Australasian Journal of Paramedicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Conjure
Kate Siklosi
2024· article· en· Explorations in Media Ecology· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affvenueaboutunlabeled
The case of the critically ill fisherman
Leah Nemiroff, Ahmed Ghaly, David Haldane, Tobias Witter
2017· article· en· Dalhousie Medical Journal· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
A response to explosion case study
K. J. Clark, Quentin A. Baker, D. E. G. Jones, D.B. Olson, R. W. Staehle
2001· article· en· Journal of Failure Analysis and Prevention· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
A Moment With...Dr. Ray Jayawardhana
Philip Mozel
2006· article· en· Journal of the Royal Astronomical Society of Canada· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
South Dakota risk pool.
Mary Carpenter
2006· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About