An Evidence‐Based Multidisciplinary Approach to the Management of Hepatocellular Carcinoma (HCC): The Alberta HCC Algorithm
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of only a few malignancies with an increasing incidence in North America. Because the vast majority of HCCs occur in the setting of a cirrhotic liver, management of this malignancy is best performed in a multidisciplinary group that recognizes the importance of liver function, as well as patient and tumour characteristics. The Barcelona Clinic Liver Cancer (BCLC) staging system is preferred for HCC because it incorporates the tumour characteristics (ie, tumour-node-metastasis stage), the patient's performance status and liver function according to the Child-Turcotte-Pugh classification, and then links the BCLC stage to recommended therapeutic interventions. However, the BCLC algorithm does not recognize the potential role of radiofrequency ablation for very early stage HCC, the expanding role of liver transplantation in the management of HCC, the role of transarterial chemoembolization in single large tumours, the potential role of transarterial radioembolization with 90Yttrium and the limited evidence for using sorafenib in Child- Turcotte-Pugh class B cirrhotic patients. The current review article presents an evidence-based approach to the multidisciplinary management of HCC along with a new algorithm for the management of HCC that incorporates the BCLC staging system and the authors' local selection criteria for resection, ablative techniques, liver transplantation, transarterial chemoembolization, transarterial radioembolization and sorafenib in Alberta.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".