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
Health Systems, Economic Evaluations, Quality of Life
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.

6,862 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.
6,862 works in the cohort · of 4,299,418page 100 of 138

Labels cover 97 of 6,862 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 6,862 of 6,862 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.

venueno affno abstractunlabeled
10.1016/s1541-9800(10)70607-4
2000· article· en· Time to knit· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Editorial
2006· editorial· es· Health Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
What Was the Goal of the Analysis?
Steven D. Stovitz, Ian Shrier, Jay S. Kaufman
2020· letter· en· The American Journal of Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1553-3212(10)70262-4
2000· article· en· Time to knit· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s1558-0164(08)70168-8
2000· article· en· Time to knit· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s1553-3212(05)71219-x
2000· article· en· Time to knit· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s1541-9800(10)70362-8
2000· article· en· Time to knit· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affno abstractunlabeled
The Value of Stress Testing in Ontario: Province or Providence?
Maria Fadous, Varsha K. Tanguturi, Jordan B. Strom
2023· editorial· en· Journal of the American Society of Echocardiography· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
In reply to “Bias assessment: mQ or RoB?”
Timothy Hugh Barker, Edoardo Aromataris, Merel Ritskes‐Hoitinga, Kim Sears, Miloslav Klugar, Jo Leonardi‐Bee +1 more
2023· letter· en· JBI Evidence Synthesis· Economics, Econometrics and Finance
machine prediction:candidate · metaresearchconsensus · none
0
citations
affaboutunlabeled
In Reply
Daniel I. McIsaac, Colin J. L. McCartney, Carl van Walraven
2017· letter· en· Anesthesiology· Economics, Econometrics and Finance
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Taking a SUMO off a TRP for bad conduct
Mahmoud A. Pouladi
2010· letter· en· Clinical Genetics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
afffundaboutunlabeled
Response to Rubanovich et al.
Robin Z. Hayeems, Stephanie Luca, Wendy J. Ungar, Ayushi Bhatt, Lauren Chad, Eleanor Pullenayegum +1 more
2019· letter· en· Genetics in Medicine· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
By the Numbers: Measuring for Quality Care
Jane Coutts
2010· article· en· Healthcare Quarterly· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Biomarkers in population-based studies
Dana A. Glei, M. Weinstein
2010· review· en· Canadian Medical Association Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Maritime provinces symposium on health technology assessment
Michael Allen, Ingrid Sketris, Ethel Langille Ingram, Lisa Farrell
2007· article· en· Journal of Continuing Education in the Health Professions· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About