NON-ALCOHOLIC FATTY LIVER DISEASE: AN UPDATE ON DIAGNOSIS
Bibliographic record
Abstract
BACKGROUND AND AIM: The non-alcoholic fatty liver disease (NAFLD) and its sub-entity, the non-alcoholic steatohepatitis (NASH) represent a field of a tremendous progress in recent years. Clinicians need to remain updated with new data on pathogenesis and therapy. The present mini review aims to present some new scientific reports on the diagnosis of NAFLD and NASH for clinical practitioners. METHODS: A systematic literature search of the main international databases was performed. We looked for seminal and innovative papers published in main international languages. A narrative review of the topic was consequently written. RESULTS: This review describes new data on the diagnosis of NAFLD including NASH. Liver punction biopsy remains the gold standard. However many patients and clinicians prefer to use noninvasive methods. We present the serological tests and the imaging methods used to diagnose inflammation and fibrosis occurring in NAFLD and NASH. CONCLUSIONS: NAFLD-NASH are multifaceted entities that have to be diagnosed and treated by skilled and informed practitioners.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".