Comparing the Effects of Atorvastatin on LDL Reduction through Systematic Review Method and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND & GOAL: LDL is one of the important risk factors that cause cardiovascular diseases. Improving one's lifestyle accompanied by taking medicine can be effective in reducing the amount of LDL. This study aims at comparing the effects of Atorvastatin on reducing LDL using systematic review method and meta-analysis. MATERIALS & METHODS: In this systematic review, Pub Med, Scopus, Elsevier and Google Scholar search engine were applied to do a search within the time span of 2003-2014 using standard key words. Articles which met the entrance criteria were studied. Random effects model was used to integrate the results through meta-analysis. Data were analyzed using Stata software (version 11.1). FINDINGS: In a systematic review, 11 articles were passed through the process of meta-analysis with a sample size of 3662 individuals and a mean of 333 subjects per study. The rate of LDL reduction was 33.32 milligrams per Deciliter by Atorvastatin. The meta-regression graph based on age showed that in the studies where the age of the sample has been younger, the amount of LDL has been higher and in studies with older sample age, the LDL rate has been lower. The meta-regression graph to BMI showed that people with higher BMI, showed more reduction in LDL and individuals with lower BMI, indicated less LDL reduction. CONCLUSION: According to the results of the study, LDL reduction takes place better and more quickly in the elderly and fat individuals. Atorvastatin was more effective in reducing the rate of LDL.
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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 | 0.040 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".