Bibliographic record
Abstract
Surveillance for hepatocellular carcinoma (HCC) in patients with recognized risk factors remains controversial. The populations for whom surveillance may be appropriate include all patients with established cirrhosis, and hepatitis B (HBV) carriers, even in the absence of cirrhosis. However, even these risk groups can be stratified into patients with higher or lower risk. The most appropriate surveillance test is periodic ultrasound examination, although the optimum screening interval has not been defined. Alphafetoprotein (AFP) is a poor surveillance test, lacking in sensitivity and specificity. There are no randomized controlled trials confirming that surveillance for HCC reduces disease-specific mortality. Modeling studies, however, have suggested that screening is cost-effective and reduces group mortality by a small amount. The criteria by which cancer surveillance programs in general can be judged have been described. Surveillance for HCC meets some of these criteria, but not all. In particular, more effective treatments have to be developed to improve the outcome of surveillance. Although there is no firm evidence to support the practice of surveillance for HCC, this has become common practice, forever preventing the definitive study from being performed. Nonetheless, surveillance is recommended in order to identify patients with small HCCs, who can be entered into trials of therapy of these tumors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".