Clinical Assessment of the Adult Patient with Possible Liver Disease: History and Physical Examination
Bibliographic record
Abstract
History • Current illness; are the symptoms suggestive of or compatible with liver disease? • Past medical history: anything associated with liver disease? • Review of systems: are there symptoms/signs suggestive of or compatible with liver disease? • Family: is there any known liver disease in the family? • Medications: list all prescription/non-prescription/alternative medicines. • Risk factors: high-risk sexual behavior, alcohol and illicit drug use, transfusions, piercing, tattoos, travel or history of living in the “developing world”. • Social: occupation, social support? Physical examination • General condition/vital signs: jaundice, malnutrition, muscle wasting, low blood pressure/elevated heart rate • Neurocognitive status: hepatic encephalopathy(hepatic fetor, “liver” flap) • Abdomen: liver (size, consistency, tenderness), spleen size, ascites, prominent abdominal wall veins/ caput medusae, hernias • Skin/nails: spider nevi, palmar erythema, Dupuytren's contractures, clubbing • Heart and lungs: hepatic hydrothorax, elevated jugular venous pressure • Miscellaneous:gynecomastia, parotid hypertrophy, peripheral edema, peripheral polyneuropathy
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".