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Record W4401057693 · doi:10.1093/qjmed/hcae144

Statin intolerance and the drucebo effect

2024· article· en· W4401057693 on OpenAlexaff
B Mugawar, Sarah McErlean, P. O. Connor, Cormac Kennedy

Bibliographic record

VenueQJM · 2024
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsTrinity College
Fundersnot available
KeywordsStatinMedicineAtherosclerotic cardiovascular diseaseInternal medicineExercise intoleranceDiseaseRisk factorCardiologyPhysical therapy

Abstract

fetched live from OpenAlex

Hypercholesterolemia is a well-described risk factor for atherosclerotic cardiovascular disease. Statins remain the cornerstone of therapy. Statin intolerance (SI) particularly statin associated muscle symptoms (SAMS) and inappropriate stopping of treatment is associated with increased cardiovascular risk. A significant proportion of reported SAMS relates to expectation of side effects and can be termed the 'negative drucebo effect'. Patients should be educated about SI, the negative drucebo effect, in addition to the benefits of adherence to the therapy when first prescribed a statin. The aim of this commentary is to discuss the issue of SI, the negative drucebo effect and to suggest some interventions that may be used to address this issue.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0120.011
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.260
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes1
Has abstractyes

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