Benefits of the Therapy With Abexol in Patients With Non-Alcoholic Fatty Liver Disease
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of diseases ranging from steatosis to steatohepatitis and cirrhosis. Given the increasing incidence of NAFLD and the long-term consequences of this disease, it is important to identify the risk factors and therapeutic measures. Abexol is a mixture of beeswax alcohols with antioxidant, gastro-protective and anti-inflammatory effects. The aim was to conduct a pooled analysis of clinical trials data of the effects of Abexol treatment in patients with NAFLD. METHODS: The present analysis includes the data of all patients with NAFLD obtained from medium-term randomized, double-blinded, placebo controlled clinical studies with Abexol. One hundred patients with NAFLD received Abexol (100 mg/day) or placebo for 6 months. Significant changes in the ultrasound analysis of the liver were considered a primary efficacy variable. Secondary endpoints were decreased homeostasis model assessment (HOMA) index and insulin levels, and improved clinical symptoms. Statistical analysis of all data was according to the intention-to-treat method. RESULTS: Both groups were statistically homogeneous at baseline conditions. At 6 months of treatment, the number of Abexol-treated patients exhibiting a normal liver echo pattern on ultrasonography was greater than that of the placebo patients (P < 0.05). Abexol significantly reduced (P < 0.05) insulin levels and HOMA index. The proportion of Abexol patients showing symptom improvement was higher (P < 0.01) than that of the placebo group. Treatments were safe and well tolerated. CONCLUSIONS: Treatment of Abexol during 6 months significantly ameliorates liver fat accumulation and insulin resistances, meanwhile improving clinical evolution in patients with NAFLD. The treatment was safe and well tolerated in these 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".