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Record W4414984049 · doi:10.5334/gh.1484

Fifteen Years of Advancing Cardiovascular Rehabilitation in Low-Resource Settings through the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR) and a Look Ahead

2025· article· en· W4414984049 on OpenAlexaffabout
Abraham Samuel Babu, Sherry L. Grace, Dion Candelaria, Robyn Gallagher, Aashish Contractor, Carley O’Neill, John Buckley, Gabriela Lima de Melo Ghisi

Bibliographic record

VenueGlobal Heart · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsAcadia UniversityToronto Rehabilitation InstituteYork UniversityUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsRehabilitationAuditCertificationGlobal healthService delivery frameworkHeart diseaseCanadian Cardiovascular SocietyDisease

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, with a particular burden in middle-income countries (MICs). Cardiac rehabilitation (CR) is a secondary prevention model resulting in reduced CV mortality, morbidity, cost-effectively. However, CR is under-utilized globally, especially in MICs due to structural, social, and economic barriers. The International Council of Cardiovascular Prevention and Rehabilitation (ICCPR) is a World Heart Federation-affiliated umbrella association founded ~15 years ago, now comprised of 50 Associations and 30 champions in countries without CR societies. ICCPR addresses delivery challenges through: CR guidelines tailored for MICs, the Global CR Audit to support advocacy, the International CR Registry (ICRR), Program Certification to support service quality, multi-disciplinary provider training (CR Foundations Certification; CRFC), women-focused CR initiatives, and partnerships with the World Health Organization. ICCPR continues to foster global CR accessibility through collaboration, communication, as well as research and advocacy with their upcoming Global CR Audit Update.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.290
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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