Bibliographic record
Abstract
The advent and efficacy of surveillance for hepatocellular carcinoma (HCC) has necessitated the refinement of assessing who is at risk for this cancer. Initially, risk was assessed for all individuals with hepatitis B and all those with cirrhosis. However, the majority of these individuals do not develop HCC so that providing surveillance for all is a waste of resources. There are now many different scores that have been developed that allow better identification of who is at risk and who is not. Specific models have been developed for hepatitis B before and on treatment, for hepatitis C before and after treatment, and for cirrhosis in general. There are also models for assessing risk in the general population. Some models can only be applied to patients coming from the population in which the score was developed (e.g., hepatitis B). Others are more generalizable. Many lack external validation. With some exceptions, the models do not attempt to assess the score at which surveillance should start. Overall, the models provide some useful guidance as to who does not need to undergo surveillance, but the long-term performance and how changes in risk score correlate with changes in HCC risk has not been completely assessed.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".