Hepatitis-Associated Liver Cancer: Gaps and Opportunities to Improve Care: Table 1.
Bibliographic record
Abstract
The global burden of hepatocellular carcinoma (HCC; primary liver cancer) is increasing. HCC is often unaccompanied by clear symptomatology, causing patients to be unaware of their disease. Moreover, effective treatment for those with advanced disease is lacking. As such, effective surveillance and early detection of HCC are essential. However, current screening and surveillance guidelines are not being fully implemented. Some at-risk populations fall outside of the guidelines, and patients who are screened are often not diagnosed at an early enough stage for treatment to be effective. From March 17 to 19, 2015, the Hepatitis B Foundation sponsored a workshop to identify gaps and limitations in current approaches to the detection and treatment of HCC and to define research priorities and opportunities for advocacy. In this Commentary, we summarize areas for further research and action that were discussed throughout the workshop to improve the recognition of liver disease generally, improve the recognition of liver cancer risk, and improve the recognition that screening for HCC makes a life-saving difference. Participants agreed that primary prevention of HCC relies on prevention and treatment of viral hepatitis and other underlying etiologies. Earlier diagnosis (secondary prevention) needs to be substantially improved. Areas for attention include increasing practitioner awareness, better definition of at-risk populations, and improved performance of screening approaches (ultrasound, biomarkers for detection, risk stratification, targeted therapies). The heterogeneous nature of HCC makes it unlikely that a single therapeutic agent will be universally effective. Medical management will benefit from the development of new, targeted treatment approaches.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".