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Record W2502158991 · doi:10.1016/j.acmx.2016.06.004

El SPRINT en la práctica médica: ¿es momento de modificar el manejo de la hipertensión arterial en Latinoamérica?

2016· article· es· W2502158991 on OpenAlexaboutno aff
José Gálvez-Olórtegui, Yudy Condor-Rojas, Tomas Gálvez-Olortegui, Luis Arturo Camacho‐Saavedra

Bibliographic record

VenueArchivos de cardiología de México · 2016
Typearticle
Languagees
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSprintGuidelineMedicineBlood pressureLatin AmericansClinical PracticePhysical therapyIntensive care medicineInternal medicinePolitical science

Abstract

fetched live from OpenAlex

This paper analyzes the feasibility of the implementation of SPRINT trial results, the need to rethink the clinical practice guidelines(CPG) for the management of arterial hypertension and associated costs with daily practice applicability. SPRINT is a clinical trial comparing systolic blood pressure control <120mmHg and <140mmHg over cardiovascular complications, generating a great worldwide impact followed by publication of several studies that addressed relevance, usefulness, applicability and controversial aspects of SPRINT from different perspectives. Achieving blood pressure goals is one of the most discussed issue in widely used hypertension CPG around the world and in Latin American. SPRINT has generated and will generate a great impact on CPG, being necessary the reassessment of blood pressure goals and inclusion in future CPG, as has been considered in 2016 Canadian guideline and will be considered in NICE guideline update scheduled for June. The SPRINT trial raises new evidence for the management of hypertension, useful in people over 50 years, from urban populations, with defined cardiovascular risk without associated comorbidities. The applicability of SPRINT in Latin America is limited by increased costs associated with hypertensive patients' integrated health care, low care coverage, and lack of integrated care programs.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.287
Teacher spread0.269 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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