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Evidence‐based care for the elderly with isolated systolic hypertension

2005· review· en· W2053623598 on OpenAlexaff
Julia Wong, Shirley Wong

Bibliographic record

VenueNursing and Health Sciences · 2005
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineArterial stiffnessBlood pressureCardiologyInternal medicineEpidemiologyVascular resistanceCompliance (psychology)Intensive care medicine

Abstract

fetched live from OpenAlex

This paper reviews the epidemiology, pathophysiology and clinical significance of isolated systolic hypertension (ISH) in the elderly. Aging is associated with structural and functional changes in the arterial tree. Intimal thickening, migration of small muscle cells to the intima, medial fibrosis, and elastic fiber degeneration result in increased arterial stiffness and ISH. The augmented systemic vascular resistance in the elderly is mediated by increased arterial stiffness. Aging is correlated with overactivity of the sympathetic nervous system, reduced neuronal plasma norepinephrine uptake, and baroreceptor dysfunction. These functional changes all contribute to the development of ISH in elderly persons. Prospective and epidemiological studies have demonstrated that ISH is associated with coronary and cerebrovascular morbidity and mortality. There is good evidence indicating that lifestyle modifications such as weight reduction, increased physical activity, moderation of dietary sodium, and decreased alcohol intake, in combination with pharmacological therapy can effectively reduce blood pressure in elderly individuals with ISH. Primary health care providers can make significant contributions to the care of elderly persons with ISH. These contributions involve educating elderly people to control hypertension through lifestyle modification, monitoring the efficacy of antihypertensive therapy, and preventing complications associated with non-compliance with therapeutic regimens.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.343
GPT teacher head0.458
Teacher spread0.115 · 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 designOther design
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

Citations9
Published2005
Admission routes1
Has abstractyes

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