The Relationship Between Nonalcoholic Fatty Liver Disease and Hepatitis B: Ameliorating or Aggravating? A Systematic Review.
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Chronic Hepatitis B is a global health challenge which has persisted despite universal vaccination against Hepatitis B. The relationship between hepatitis B virus infection and Non-alcoholic Fatty disease (NAFLD) remains unclear thus, a review was carried out to elucidate the nature of the relationship existing between them and the risk factors for this interrelation. DATA SOURCE AND SELECTION: The accepted guideline for a systematic review was followed. English language-based studies on hepatitis B and NAFLD in adult populations between 2010 -2021 were sourced from CINHAL, PubMed, Medline, Scopus, google scholar and ScienceDirect database. DATA EXTRACTION: Following the PICO format, studies which met the inclusion criteria were identified and selected on a Prisma chart. They were further assessed using the modified Newcastle-Ottawa score for the assessment of non-randomized studies. RESULT: 11 out of 12,380 studies obtained from multiple databases were included in the review comprising of 128,566 controls and 5177 cases. The relationship between exposure to hepatitis B infection and NAFLD outcome was aggravating, ameliorating and non-existent in six, four and one study respectively. Risk factors for NAFLD identified include metabolic factors such as increased body mass index, hyperglycaemia, raised triglycerides, metabolic syndrome, hyperuricemia and the presence of hepatitis B HBx protein. CONCLUSION: NAFLD is most likely to occur in HBV patients in the presence of host metabolic factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".