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Record W4285042463 · doi:10.1038/s41371-022-00706-9

Country experiences on the path to exclusive use of validated automated blood pressure measuring devices within the HEARTS in the Americas Initiative

2022· article· en· W4285042463 on OpenAlexaff
Cintia Lombardi, Dean S. Picone, James E. Sharman, Norm R.C. Campbell, Rafael Feldmann Farias, Stephanie Guerre, Anselm Gittens, Mélanie Paccot, Nilda Villacres, Yamilé Valdés, Pedro Ordúñez

Bibliographic record

VenueJournal of Human Hypertension · 2022
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineProcurementControl (management)Set (abstract data type)Public healthHealth careBest practiceBlood pressureProcess managementEconomic growthNursingBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

The aim of the HEARTS in the Americas initiative is to promote the adoption of global best practices in the prevention and control of cardiovascular diseases, and improve the control of hypertension. HEARTS is being implemented in 21 countries and a diverse set of actions and measures are in progress to improve exclusive access in primary health care facilities to automated blood pressure measuring devices that have been validated for accuracy. The purpose of this manuscript is to illustrate these efforts, mainly in the regulatory and public procurement arena, and to present information on common challenges and solutions identified. Examples from six countries confirm the need for not only a robust regulatory framework to increase availability of validated automated blood pressure measuring devices but also a comprehensive strategic approach that involves relevant stakeholders, includes a multi-pronged approach and is associated with a national program to prevent and control non communicable 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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.117
GPT teacher head0.310
Teacher spread0.193 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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