Step‐by‐step diagnosis and management of the nocebo/drucebo effect in statin‐associated muscle symptoms patients: a position paper from <i>the International Lipid Expert Panel</i> (ILEP)
Bibliographic record
Abstract
Statin intolerance is a clinical syndrome whereby adverse effects (AEs) associated with statin therapy [most commonly statin-associated muscle symptoms (SAMS)] result in the discontinuation of therapy and consequently increase the risk of adverse cardiovascular outcomes. However, complete statin intolerance occurs in only a small minority of treated patients (estimated prevalence of only 3-5%). Many perceived AEs are misattributed (e.g. physical musculoskeletal injury and inflammatory myopathies), and subjective symptoms occur as a result of the fact that patients expect them to do so when taking medicines (the nocebo/drucebo effect)-what might be truth even for over 50% of all patients with muscle weakness/pain. Clear guidance is necessary to enable the optimal management of plasma in real-world clinical practice in patients who experience subjective AEs. In this Position Paper of the International Lipid Expert Panel (ILEP), we present a step-by-step patient-centred approach to the identification and management of SAMS with a particular focus on strategies to prevent and manage the nocebo/drucebo effect and to improve long-term compliance with lipid-lowering therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".