Coenzyme Q<sub>10</sub>and selenium in statin-associated myopathy treatment
Bibliographic record
Abstract
The objective of this study was to evaluate the possible benefits of coenzyme Q10 and selenium supplementation administered to patients with statin-associated myopathy (SAM). Sixty eligible patients entered the pilot study. Laboratory examination (CoQ10, selenium, creatin kinase) and intensity of SAM (visual scale) were performed at baseline, after 1 month, and at the end of study at month 3. Plasma levels of CoQ10 increased from 0.81 ± 0.39 to 3.31 ± 1.72 μmol/L in the active group of patients treated by CoQ10, compared with the placebo (p = 0.001). Also, the symptoms of SAM significantly improved in the active group (p < 0.001): the intensity of muscle pain decreased from 6.7 ± 1.72 to 3.2 ± 2.1 (p < 0.01, -53.4 ± 28.2%); muscle weakness decreased from 7.0 ± 1.63 to 2.8 ± 2.34 (p < 0.01, -60 ± 24.0%); muscle cramps decreased from 5.33 ± 2.06 to 1.86 ± 2.42, p < 0.01, -65 ± 28%); tiredness decreased from the initial 6.7 ± 1.34 to 1.2 ± 1.32 (p < 0.01, -82 ± 22%). We did not observe any significant changes in the placebo group. In conclusion, supplementation of statin-treated patients with CoQ10 resulted in a decrease in the symptoms of SAM, both in absolute numbers and intensity. Additional selenium supplementation was not associated with any statistically significant decrease of SAM. However, it is not possible to draw any definite conclusions, even though this study was carried out in double-blind fashion, because it involved a small number of patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".