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Record W4377220804 · doi:10.1080/14779072.2023.2215982

Statin associated muscle symptoms (SAMS): strategies for prevention, assessment and management

2023· review· en· W4377220804 on OpenAlexafffund
Iulia Iatan, G.B. John Mancini, Eunice Yeoh, Robert A. Hegele

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2023
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern UniversitySt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicineStatinIntensive care medicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Statins are the cornerstone for atherosclerotic cardiovascular disease risk reduction with recognized efficacy in primary and secondary prevention. Despite this, they remain underutilized due to concerns regarding adverse effects. Statin-associated muscle symptoms (SAMS) are the most frequent cause of medication intolerance and discontinuation with a prevalence estimated at 10%, regardless of causality, with the consequence of increased risk of adverse cardiovascular outcomes. AREAS COVERED: This clinical perspective reviews recent developments in mechanisms underlying the pathogenesis of statin myopathy, the role of the nocebo effect in perception of statin intolerance, and explores diverse components endorsed by international societies in establishing a statin intolerance syndrome. Non-statin drug alternatives that reduce low-density lipoprotein-cholesterol are also discussed, with emphasis on therapies with established effects on cardiovascular outcomes. EXPERT OPINION: Ultimately, a patient-centered clinical approach to managing SAMS is proposed to optimize statin tolerability, achieve guideline-recommended therapeutic goals and improve cardiovascular 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)0.005
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 score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.009
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 head0.066
GPT teacher head0.417
Teacher spread0.351 · 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

Citations19
Published2023
Admission routes2
Has abstractyes

Explore more

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