The place of downstaging for hepatocellular carcinoma
Bibliographic record
Abstract
In the treatment of hepatocellular carcinomas, therapies such as trans-arterial chemo-embolisation, trans-arterial radioembolisation, percutaneous ethanol injection and radio-frequency ablation can decrease the size (and overall viability) of the tumours, thus potentially increasing the proportion of patients qualifying for resection and transplantation. While the use of such downstaging therapies is straightforward when resection is the aim, in a similar way to other neo-adjuvant treatments in the surgery of tumours that are too large or awkwardly placed to be primarily resected the issues related to transplantation are more complex. In the context of transplantation the word "downstaging" designates not only a neo-adjuvant treatment, but also a selection strategy to allow patients who are initially outside accepted listing criteria to benefit from transplantation should the neo-adjuvant therapy be successful in reducing tumour burden. The effectiveness of downstaging as a selection strategy, at first questioned because of methodological bias in the studies that described it, has been recently demonstrated by more solid prospective investigations. Several issues however remain open, such as inclusion criteria before the strategy is implemented (size/number, surrogate markers of differentiation/vascular invasion such as alpha-fetoprotein), the choice of which downstaging therapy, the end-points of treatment, and the need and duration of a period of observation proving disease response or stabilisation before the patient can be listed. The present review discusses which treatments and strategies are available for downstaging HCC on the basis of the published literature.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".