Bibliographic record
Abstract
The liver enzymes, alanine transaminase (ALT) or aspartate transaminase (AST), are commonly used in clinical practice as screening as well as diagnostic tests for liver diseases. ALT is more specific for liver injury than AST and has been shown to be a good predictor of liver related and all-cause mortality. Asymptomatic mild hypertransaminasemia (i.e., less than five times normal) is a common finding in primary care and this could be attributed to serious underlying condition or has transient and benign cause. Unfortunately, there are no good literatures available on the cost-effectiveness of evaluating patients with asymptomatic mild hypertransaminasemia. However, if the history and physical examination do not suggest a clear cause, a stepwise approach should be initiated based on pretest probability of the underlying liver disease. Nonalcoholic fatty liver disease is becoming the most common cause of mild hypertransaminasemia worldwide. Other causes include alcohol abuse, medications, and hepatitis B and C. Less common causes include hemochromatosis, α1-antitrypsin deficiency, autoimmune hepatitis, and Wilson's disease. Nonhepatic causes such as celiac disease, thyroid, and muscle disorders should be considered in the differential diagnosis. Referral to a specialist and a possible liver biopsy should be considered if persistent hypertransaminasemia for six months or more of unclear etiology.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".