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Record W4410259767 · doi:10.26633/rpsp.2025.43

Expanding team-based care for hypertension and cardiovascular risk management with HEARTS in the Americas

2025· article· en· W4410259767 on OpenAlexaff
Vilma Irazola, Carolina Prado, Andrés Rosende, David Flood, Ross T. Tsuyuki, Carolina Neira Ojeda, Minerva Jiménez Reyes, Johanna Otero, Irmgardt Alicia Wellmann, Ileana Fajardo, Emily Ridley, Eduardo Londoño, Gloria Giraldo, Edwin Bolastig, Bruna Moreno Dias, Nicolas Haeberer, Pedro Ordúñez

Bibliographic record

VenueRevista Panamericana de Salud Pública · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineLatin AmericansHealth careNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Cardiovascular diseases remain the leading cause of premature morbidity and mortality globally, with hypertension as their main modifiable risk factor. In Latin America and the Caribbean, hypertension affects more than 30% of adults, yet control rates remain alarmingly low. The HEARTS in the Americas Initiative, led by the Pan American Health Organization, promotes a model of team-based care to enhance risk management for hypertension and cardiovascular diseases within primary health care. Team-based care leverages the skills of diverse health professionals, including nurses, pharmacists and community health workers, to optimize resource allocation, task-sharing and care delivery. Evidence underscores the effectiveness of team-based care in improving blood pressure control, reducing hospitalizations and enhancing quality of life through strategies such as periodic follow up and medication titration. Despite its benefits, implementing team-based care faces cultural and systemic barriers. This special report outlines a policy framework to scale team-based care across the Region of the Americas, ensuring equitable access to high-quality, cost-effective prevention and care for cardiovascular diseases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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 designNot applicable
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

Citations11
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRevista Panamericana de Salud PúblicaSame topicBlood Pressure and Hypertension StudiesFrench-language works237,207