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Record W2323546411 · doi:10.1097/hco.0000000000000067

Prevention and control of hypertension

2014· review· en· W2323546411 on OpenAlexafffund
Norm R.C. Campbell, Mark L. Niebylski

Bibliographic record

VenueCurrent Opinion in Cardiology · 2014
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicinePsychological interventionHealth careIntensive care medicineDiseaseChronic diseaseControl (management)NursingEconomic growth

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review demonstrates the need for enhancing strategic approaches to the prevention and control of hypertension, a global health issue. RECENT FINDINGS: An epidemic of chronic noncommunicable diseases is threatening national healthcare systems' sustainability and the economy of many countries. Increased blood pressure is the leading risk for premature death and disability and accounts for approximately 10% of healthcare spending. Four of nine recent United Nations' targets for reducing chronic noncommunicable diseases relate directly or indirectly to hypertension. The expanded chronic care model provides a comprehensive framework for developing hypertension prevention and control strategies. The model addresses the roles of healthy public policy, healthy living environments, healthy communities, reorientation of health services delivery toward management of chronic illness, support for improving clinical decisions, enhanced skills of people to prevent and self-manage chronic disease, partnerships of stakeholders and information systems to track the impact of interventions and identify care 'gaps'. The authors advocate that hypertension organizations can lead aspects of hypertension strategy development and implementation. SUMMARY: Prevention and control of hypertension requires a strategic approach that could have a central role for hypertension experts and the hypertension community.

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: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
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 head0.180
GPT teacher head0.405
Teacher spread0.225 · 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

Citations40
Published2014
Admission routes2
Has abstractyes

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