Multidisciplinary Canadian consensus recommendations for the management and treatment of hepatocellular carcinoma
Bibliographic record
Abstract
Globally, hepatocellular carcinoma (hcc) is the third most common cause of death from cancer, after lung and stomach cancer. The incidence of hcc in Canada is increasing and is expected to continue to increase over the next decade. Given the high mortality rate associated with hcc, steps are required to mitigate the impact of the disease. To address this challenging situation, a panel of 17 hcc experts, representing gastroenterologists, hepatologists, hepatobiliary surgeons, medical oncologists, pathologists, and radiologists from across Canada, convened to provide a framework that, using an evidence-based approach, will assist clinicians in optimizing the management and treatment of hcc. The recommendations, summarized here, were developed based on a rigorous methodology in a pre-specified process that was overseen by the steering committee. Specific topics were identified by the steering committee and delegated to a group of content experts within the expert panel, who then systematically reviewed the literature on that topic and drafted the related content and recommendations. The set of recommendations for each topic were reviewed and assigned a level of evidence and grade according to the levels of evidence set out by the Centre for Evidence-based Medicine, Oxford, United Kingdom. Agreement on the level of evidence for each recommendation was achieved by consensus. Consensus was defined as agreement by a two-thirds majority of the 17 members of the expert panel. Recommendations were subject to iterative review and modification by the expert panel until consensus could be achieved.
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 | 0.048 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".