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Record W2884485392 · doi:10.1186/s40985-018-0101-z

Screening and treatment of hypertension in older adults: less is more?

2018· review· en· W2884485392 on OpenAlexaff
Daniela Anker, Brigitte Santos‐Eggimann, Valérie Santschi, Cinzia Del Giovane, Christina Wolfson, Nicolas Rodondi, Arnaud Chioléro

Bibliographic record

VenuePublic health reviews · 2018
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineBlood pressureStroke (engine)Observational studyDiseasePopulationHeart failureInternal medicineGerontologyPhysical therapyPediatricsEnvironmental health

Abstract

fetched live from OpenAlex

Screening and treatment of hypertension is a cornerstone of cardiovascular disease (CVD) prevention. Hypertension causes a large proportion of cases of stroke, coronary heart disease, heart failure, and associated disability and is highly prevalent especially among older adults. On the one hand, there is robust evidence that screening and treatment of hypertension prevents CVD and decreases mortality in the middle-aged population. On the other hand, among older adults, observational studies have shown either positive, negative, or no correlation between blood pressure (BP) and cardiovascular outcomes. Furthermore, there is a lack of high quality evidence for a favorable harm-benefit balance of antihypertensive treatment among older adults, especially among the oldest-old (i.e., above the age of 80 years), because very few trials have been conducted in this population. The optimal target BP may be higher among older treated hypertensive patients than among middle-aged. In addition, among frail or multimorbid older individuals, a relatively low BP may be associated with worse outcomes, and antihypertensive treatment may cause more harm than benefit. To guide hypertension screening and treatment recommendations among older patients, additional studies are needed to determine the most efficient screening strategies, to evaluate the effect of lowering BP on CVD risk and on mortality, to determine the optimal target BP, and to better understand the relationship between BP, frailty, multimorbidity, and health outcomes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.000
Bibliometrics0.0000.001
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 head0.371
GPT teacher head0.422
Teacher spread0.051 · 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 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

Citations56
Published2018
Admission routes1
Has abstractyes

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