Initial Diagnosis, Workup, and Assessment of Severity of Liver Disease in Adults
Bibliographic record
Abstract
A complete history and physical examination will indicate to the clinician several clues with regard to both etiology and severity of any liver disease. Initial assessment and workup of liver disease involves widely available blood tests to determine hepatocellular versus cholestatic liver disease. Liver function tests such as INR and total bilirubin confirm the degree of liver synthetic dysfunction and indicate the need for liver transplantation, particularly in those with acute liver failure. A carefully performed abdominal ultrasound can detect, but not exclude, cirrhosis. Evaluation of the degree of liver dysfunction can be made with simple blood tests. At present, liver biopsy, despite its limitations, remains the gold standard for evaluation of hepatic fibrosis. Non-invasive testing, including FibroTest and FibroScan, may reduce the need for biopsy, but they have not been validated across the spectrum of liver disease. Child–Turcotte–Pugh and Model for End-stage Liver Disease scores offer the best estimate of survival of cirrhotic patients and are used as a guide for the timing of transplant for non-malignant liver disease and risk assessment for perioperative complications for those who have liver disease but require non-hepatic surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".