Bibliographic record
Abstract
Outside of expert centres, surveillance programmes for hepatocellular carcinoma (HCC) are not well executed. There are deficiencies in every stage of the process. Overcoming these obstacles is the most important method for improving surveillance. However, even if these obstacles were overcome, there would still be room for improvement. Assessing who is at risk of developing HCC remains incompletely validated. At present, risk scores have been developed for different causes of liver disease, but scores developed in different parts of the world for the same disease do not always agree. Furthermore, most scores stratify patients by risk but do not examine what level of risk should trigger surveillance. Which surveillance tools to use remains controversial - schemes have been proposed that use biomarkers alone, ultrasound alone, or a combination of both. However, the requisite level of test sensitivity that would be associated with high cure rates has not been defined, so at this point it is not clear whether surveillance requires both ultrasound and biomarkers, or whether the use of biomarkers alone is sufficient. Finally, surveillance should result in the identification of HCC at a very early stage. Diagnosing these lesions is difficult and optimal algorithms for lesions that are atypical on radiology have yet to be developed. Algorithms for the follow-up of abnormal biomarkers in the absence of ultrasound have also not been developed yet.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".