Blood pressure lowering efficacy of coenzyme Q10 for primary hypertension
Bibliographic record
Abstract
BACKGROUND: Studies have shown that coenzyme Q10 deficiency is associated with cardiovascular disease. Hypertension is a commonly measured surrogate marker for non-fatal and fatal cardiovascular endpoints such as heart attacks and strokes. Clinical trials have suggested that coenzyme Q10 supplementation can effectively lower blood pressure (BP). OBJECTIVES: To determine the blood pressure lowering effect of coenzyme Q10 in primary hypertension. SEARCH STRATEGY: The Cochrane Central Register of Controlled Trials (2009 Issue 2), MEDLINE (1966 -May 2008), EMBASE (1982 - May 2008), and CINAHL (1970 - May 2008) as well as the reference lists of articles were searched for relevant clinical trials in any language. SELECTION CRITERIA: Double-blind, randomized, placebo-controlled parallel or crossover trials evaluating the BP lowering efficacy of coenzyme Q10 for a duration of at least 3 weeks in patients with primary hypertension. DATA COLLECTION AND ANALYSIS: The primary author independently assessed the risk of bias and extracted the data. The second author verified data extraction. MAIN RESULTS: Three clinical trials with a total of 96 participants were evaluated for the effects of coenzyme Q10 on blood pressure compared to placebo. Treatment with coenzyme Q10 in subjects with systolic BP (SBP) > 140 mmHg or diastolic BP (DBP) > 90 mmHg resulted in mean decreases in SBP of 11 mmHg (95% CI 8, 14) and DBP of 7 mmHg (95% CI 5, 8). AUTHORS' CONCLUSIONS: Due to the possible unreliability of some of the included studies, it is uncertain whether or not coenzyme Q10 reduces blood pressure in the long-term management of primary hypertension.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".