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Record W4214834440 · doi:10.1016/j.lana.2022.100219

2021 World Health Organization guideline on pharmacological treatment of hypertension: Policy implications for the region of the Americas

2022· review· en· W4214834440 on OpenAlexaffabout
Norm R.C. Campbell, Mélanie Paccot, Paul K. Whelton, Sonia Y. Angell, Marc G. Jaffe, Jennifer Cohn, Alfredo Espinosa Brito, Vilma Irazola, Jeffrey Brettler, Edward J. Roccella, Javier Isaac Maldonado Figueredo, Andrés Rosende, Pedro Ordúñez

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2022
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsGuidelineMedicineDiseaseDisease controlBlood pressureControl (management)Quarter (Canadian coin)GerontologyFamily medicinePolitical scienceIntensive care medicineEnvironmental healthManagementPathologyInternal medicineGeography

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is the leading cause of death in the Americas and raised blood pressure accounts for over 50% of CVD. In the Americas over a quarter of adult women and four in ten adult men have hypertension and the diagnosis, treatment and control are suboptimal. In 2021, the World Health Organization (WHO) released an updated guideline for the pharmacological treatment of hypertension in adults. This policy paper highlights the facilitating role of the WHO Global HEARTS initiative and the HEARTS in the Americas initiative to catalyze the implementation of this guideline, provides specific policy advice for implementation, and emphasizes that an overarching strategic approach for hypertension control is needed. The authors urge health advocates and policymakers to prioritize the prevention and control of hypertension to improve the health and wellbeing of their populations and to reduce CVD health disparities within and between populations of the Americas.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.599
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.434
GPT teacher head0.490
Teacher spread0.056 · 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
GenreReview

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

Citations109
Published2022
Admission routes2
Has abstractyes

Explore more

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