Liver-directed therapy for hepatocellular carcinoma
Bibliographic record
Abstract
The high mortality rate for hepatocellular carcinoma (HCC) relative to its prevalence underscores the need for curative-intent therapies. Multidisciplinary treatment decisions are required to craft optimal treatment strategies considering tumor size, location and underlying liver cirrhosis. Surgical resection of anatomically limited tumors with adequate hepatic reserve provides long-term survival in more than half of patients and remains a standard first-line therapy. Eligibility for resection among newly diagnosed patients is low and recurrences in the remaining cirrhotic liver are common. Transplantation offers a higher chance of cure. Long wait times for the limited door pool require neoadjuvant loco-regional therapies to maintain transplant eligibility. Image-guided therapies such as ablation and embolization have an established role as primary or neoadjuvant preparing patients for curative treatment. Percutaneous ablation in appropriately selected patients offers long-term survival similar to resection. New and evolving techniques such as stereotactic body radiotherapy (SBRT), radiation segmentectomy and lobectomy, and combination therapies employing both trans-arterial and ablative approaches show promise for curative-intent treatment but require further prospective data before they can be integrated into treatment algorithms. For palliativeintent therapy, conventional trans-arterial chemoembolization with lipiodol-based emulsions remains the only technique supported by clinical trials. Newer platforms such as drug-eluting embolics failed to improve survival over bland embolization in randomized trials and showed increased hepatobiliary toxicity. Transarterial radioembolization offers similar overall survival (OS) to transarterial chemoembolization (TACE) and non-inferiority to sorafenib in meta-analyses. The more favorable clinical toxicity profile makes it an appealing technique for patients willing to accept the longer time to response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".