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
Frailty in Older Adults
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.

3,562 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.
3,562 works in the cohort · of 4,299,418page 65 of 72

Labels cover 11 of 3,562 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 3,562 of 3,562 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.

affno abstractunlabeled
PR_088
Viem C. Nguyen, William C. Miller, Miho Asano, Roger Wong
2006· article· en· Archives of Physical Medicine and Rehabilitation· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Fragility syndrome in patients with chronic heart failure
Snejana Vetrila, Eleonora Vataman, Virginia Șalaru, Anastasia Ivanes
2022· article· en· Bulletin of the Academy of Sciences of Moldova Medical Sciences· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Oncogeriatrics
2017· book-chapter· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Uso de tecnologías de asistencia y fragilidad en adultos mayores de 80 años y más / Assisting technologies and frailty in aged 80 years and older / Uso de tecnologias de assistência e fragilidade em idosos de 80 anos ou mais
E. Teixeira-Gasparini, Rosalina Aparecida Partezani Rodrigues, Suzele Cristina Coelho Fabrício-Wehbe, Jack Roberto Silva Fhon, M. Aleixo-Diniz, Luciana Kusumota
2016· article· es· Enfermería Universitaria· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Sports Update
2018· other· en· Internet Archive (Internet Archive)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
CCCN Committees/CCCN Supporters
2019· article· en· Canadian Journal of Cardiology· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Association between vulnerability, frailty and risk of falling in elderly people
Cristiane dos Santos Silva, Rodrigo Mercês Reis Fonsca, Adriano Almeida Souza, Shahjahan Mozart Alexandre da Silva Nery, David Ohara, Margarida Neves de Abreu +2 more
2024· article· en· Cuadernos de Educación y Desarrollo· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About