Non-surgical management of advanced hepatocellular carcinoma: A systematic review by Cancer Care Ontario
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) is a global health problem, accounting for 4.7% of all new cancer cases and 8.2% of all cancer deaths worldwide in 2018. Resection and transplantation are the only modalities that offer a cure for HCC; however, most patients are diagnosed at an advanced stage, precluding these curative treatments. A number of local (ie, ablative therapies) and/or local-regional therapies (ie, chemo-embolization) are used and followed by systemic therapy for advanced or progressive disease. Other treatments are available, but their efficacy compared with these standards is not well known. Methods: Literature searches (1/2000 to 1/2020 or 1/2005 to 1/2020, depending on the specific systematic review question) were conducted, including MEDLINE, Embase and the Cochrane Database of Systematic Reviews. Results: Over 30,000 articles were identified. In total, 49 studies were included in the systematic review. Conclusions: There is no evidence to support the addition of sorafenib to any local or regional therapy. First-line systemic therapy options for unresectable or metastatic HCC include sorafenib, lenvatinib, and atezolizumab + bevacizumab. Regorafenib or cabozantinib provide survival benefits when given as second-line treatment.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".