Bibliographic record
Abstract
Nonalcoholic steatohepatitis (NASH) is a histological diagnosis applied to a constellation of liver biopsy findings that develop in the absence of alcohol abuse. Steatosis, a mixed cellular inflammatory infiltrate across the lobule, evidence of hepatocyte injury and fibrosis are the findings that can be seen. This entity is often identified during evaluation of elevated aminotransferases after exclusion of viral, metabolic and other causes of liver disease. Obesity is a major risk factor for NASH. The role of diabetes is less certain, although evidence is accumulating that hyperinsulinism may play an important pathophysiological role. Patients sometimes suffer from right upper quadrant abdominal pain and fatigue; examination may reveal centripetal obesity and hepatomegaly. Although patients are often discovered because of persistent aminotransferase elevations, these enzymes can be normal in NASH. When they are elevated, the alanine aminotransferase level is typically significantly greater than the aspartate aminotransferase level. This can be particularly helpful for excluding occult alcohol abuse. Imaging studies identify hepatic steatosis when the amount of fat in the liver is significant; however, imaging does not distinguish benign steatosis from NASH. Ultimately a liver biopsy is needed to diagnose NASH. The biopsy may be useful for establishing prognosis based on the presence or absence of fibrosis and for excluding other unexpected causes of liver enzyme elevations. Weight loss is the mainstay of treatment for obese patients. About 15% to 40% of NASH patients develop fibrosis; how many of these cases progress to cirrhosis is unknown, but about 1% of liver transplants are performed with a pretransplant diagnosis of NASH.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".