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Record W2056166846 · doi:10.4137/cmc.s10628

Effects of Pharmacologic Therapy on Health-Related Quality of Life in Elderly patients with Atrial Fibrillation: A systematic Review of Randomized and Nonrandomized Trials

2013· review· en· W2056166846 on OpenAlexfundno aff
Carl J. Pepine

Bibliographic record

VenueClinical Medicine Insights Cardiology · 2013
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
FundersCanadian Cardiovascular Society
KeywordsMedicineAtrial fibrillationQuality of life (healthcare)Randomized controlled trialIntensive care medicineInternal medicineCardiology

Abstract

fetched live from OpenAlex

This systematic review assessed the impact of atrial fibrillation (AF) and pharmacotherapy on health-related quality of life (HRQOL) in elderly patients. Highly prevalent in the elderly, AF is associated with morbidity and symptoms affecting HRQOL. A PubMed and EMBASE search (1999-2010) was conducted using the terms atrial fibrillation, elderly, quality of life, Medicare, and Medicaid. In all, 504 articles were identified and 15 were selected (studies examining pharmacotherapy [rate or rhythm control] and HRQOL in AF patients with a mean age ≥ 65 years). Information, including study design, cohort size, and HRQOL instruments utilized, was extracted. Five observational studies, 5 randomized trials comparing rate and rhythm control, 3 randomized trials investigating pharmacologic agents, and 2 trials examining HRQOL, depression, and anxiety were identified. Elderly AF patients had reduced HRQOL versus patients in normal sinus rhythm, particularly in domains related to physical functioning. HRQOL may be particularly affected in older AF patients. Although data do not indicate whether a pharmacologic intervention or single treatment strategy-namely rate versus rhythm control-is better at improving HRQOL, either of these strategies and many pharmacologic interventions may improve HRQOL in elderly AF patients. Based on reviewed data, an algorithm is suggested to optimize HRQOL among elderly patients.

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)0.022
metaresearch head score (Gemma)0.086
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.086
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0570.004
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.220
GPT teacher head0.484
Teacher spread0.264 · 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 designSystematic review
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

Citations17
Published2013
Admission routes1
Has abstractyes

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