Clinical features related to statin‐associated muscle symptoms
Bibliographic record
Abstract
INTRODUCTION: Statins reduce cardiovascular disease risk and are generally well tolerated, yet up to 0.5% of statin-treated patients develop incapacitating muscle symptoms including rhabdomyolysis. Our objective was to identify clinical factors related to statin-associated muscle symptoms (SAMS). METHODS: Clinical and laboratory characteristics were evaluated in 748 statin-treated Caucasians (634 with SAMS and 114 statin-tolerant controls). Information was collected on statin type, concomitant drug therapies, muscle symptom history, comorbidities, and family history. Logistic regression was used to identify associations. RESULTS: Individuals with SAMS were 3.6 times (odds ratio [OR] 3.60, 95% confidence interval [CI] 2.08-6.22) more likely than statin-tolerant controls to have a family history of heart disease. Additional associations included obesity (OR 3.08, 95% CI 1.18, 8.05), hypertension (OR 2.24, 95% CI 1.33, 3.77), smoking (OR 2.08, 95% CI 1.16, 3.74), and statin type. DISCUSSION: Careful medical monitoring of statin-treated patients with the associated coexisting conditions may ultimately reduce muscle symptoms and lead to improved compliance. Muscle Nerve 59:537-537, 2019.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".