Hepatocellular Carcinoma: Epidemiology, Risk Factors, and Screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, the epidemiology of hepatocellular carcinoma (HCC), risk factors for the development of HCC, and how these factors affect the decision about whether an individual should or should be entered into a screening program are considered. The factors determining the risk for HCC include age, male gender, and the nature of the underlying liver disease. In particular, cirrhosis is associated with a significant risk for HCC. However, in hepatitis B HCC also occurs in noncirrhotic liver. Decision analysis can be used to identify patients at greatest risk for HCC and who might be candidates for screening. Screening itself should be developed in a programmatic manner to ensure that appropriate target populations are identified, that appropriate screening tests are chosen, and that appropriate recall and enhanced follow-up are instituted for patients who have positive screening test results. Screening should be by ultrasonography at 4- to 12-month intervals. Patients with abnormal screening tests require additional investigation using computed tomography scanning, magnetic resonance imaging, or liver biopsy. Negative results do not exclude the possibility of cancer and further follow-up is necessary.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it