Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) has progressed within the last 10–15 years from being a cancer that was almost universally fatal to one that is potentially curable in the majority of cases. However, for this to happen it is essential that the HCC is found early. Patients at risk for HCC have to undergo regular surveillance with ultrasound. Lesions detected during surveillance have to be aggressively investigated and aggressively treated. If HCC can be diagnosed when the lesion is 90%.1 2 Treated patients remain at risk for the development of a second primary, but the treated HCC can be completely cured. Ultrasound surveillance identifies many small lesions in the liver that may or may not be HCC. These include dysplastic nodules, cirrhotic nodules and haemangioma. Making the distinction between HCC and haemangioma is usually not difficult, but distinguishing between cirrhotic nodules, dysplastic nodules and HCC can be difficult. The tools available include contrast-enhanced radiological imaging, or biopsy. It is possible to diagnose HCC without biopsy. Indeed, if the typical radiological features are present, the diagnostic accuracy is almost 100%. The highly characteristic features are that in the arterial phase of a dynamic contrast-enhanced study the HCC shows hypervascularity—that is, it gives a brighter signal that the surrounding liver. In the portal venous phase of the study and in the delayed phase ∼3 min …
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".