Evidence map›Paper›PMID 31933238›Full record

ReviewPharmaceutical medicine2019

Applying Non-Invasive Fibrosis Measurements in NAFLD/NASH: Progress to Date.

Somaya Albhaisi, Arun J Sanyal

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmaceutical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Somaya AlbhaisiDepartment of Internal Medicine, Virginia Commonwealth University, Box 980102, Richmond, VA, 23298, USA.
Arun J SanyalDivision of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, Virginia Commonwealth University, Box 980341, Richmond, VA, 23298, USA. arun.sanyal@vcuhealth.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nonalcoholic fatty liver disease (NAFLD) has now become a worldwide health issue due to the obesity epidemic, affecting approximately 90% of the obese population and 15-40% of the general population. It is the most common form of chronic liver disease in the United States. NAFLD constitutes a spectrum of diseases ranging in severity from mild, such as fatty liver, progressing into nonalcoholic steatohepatitis (NASH), then fibrosis, and ending with cirrhosis. NASH and increasing fibrosis stage are associated with increased morbidity and mortality; the fibrosis stage is therefore a critical element of risk stratification needed to determine therapeutic approach and also the response to treatment. Liver biopsy is considered the 'gold standard' in the diagnosis of NAFLD. However, it is not practical for widespread clinical use because it is invasive, costly, and associated with complications including occasional death. These limitations have driven the development of noninvasive tests that can accurately predict the fibrosis stage in those with NAFLD. In this review, we provide a concise overview of different non-invasive measurements used for NAFLD/NASH.

Indexed as

BiomarkersBiopsyCirculating MicroRNADisease ProgressionElasticity Imaging TechniquesGastrointestinal MicrobiomeHumansLiverLiver CirrhosisMetabolomicsNon-alcoholic Fatty Liver DiseaseObesityObserver VariationPredictive Value of TestsPrevalenceProteomicsBiomarkersCirculating MicroRNA

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.