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Record W4283725434 · doi:10.1016/j.ahjo.2022.100166

Antihypertensive medication in frail older adults: A narrative review through a deprescribing lens

2022· review· en· W4283725434 on OpenAlexaff
Roni Kraut, Carina Lundby, Оксана Бабенко, Ahmad Kamal, Cheryl A Sadowski

Bibliographic record

VenueAmerican Heart Journal Plus Cardiology Research and Practice · 2022
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDeprescribingNarrativeMedicineLens (geology)PolypharmacyOlder peopleGerontologyOptometryIntensive care medicineArtEngineeringLiterature

Abstract

fetched live from OpenAlex

Purpose of review: The management of hypertension in frail older adults remains controversial, as these patients are underrepresented in clinical trials and practice guidelines. Overtreatment may cause harm while undertreatment may lead to greater risk of cardiovascular events. Our research aims to examine this controversy and provide guidance regarding deprescribing decisions in frail older adults. Results: Current evidence suggests that there may be minimal cardiovascular benefit and significant harm of antihypertensive medication in the frail older adult population. A minority of hypertension guidelines provide sufficient recommendations for frail older adults, and there are limited tools available to guide clinical decision-making. Conclusion: Randomized controlled trials and well-designed observational studies are needed to confirm the benefit-to-harm relationship of antihypertensive medication in frail older adults. Decision tools that comprehensively address antihypertensive deprescribing would be advantageous to help clinicians with hypertension management in this population. Clinicians should engage in shared decision-making with the patient and family to ensure that decisions regarding antihypertensive deprescribing best meet the needs of all involved.

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)0.007
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.006
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 head0.252
GPT teacher head0.484
Teacher spread0.232 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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