Overcoming disparities in hepatocellular carcinoma outcomes in First Nations Australians: a strategic plan for action
Bibliographic record
Abstract
Overcoming disparities in hepatocellular carcinoma outcomes in First Nations Australians: a strategic plan for action E very year, about 1800 Australians die of hepatocellular carcinoma (HCC), the most common type of primary liver cancer. 1 Aboriginal and Torres Strait Islander peoples of Australia (hereon respectfully referred to as First Nations Australians) are 2.5 times more likely to develop HCC and 1.4 times more likely to die from HCC than non-Indigenous Australians. 2 First Nations Australians with HCC have a 9% five-year survival rate compared with 23% for non-Indigenous Australians, 2 and are half as likely to be diagnosed with early-stage HCC and receive curative therapy. 2 This is driven by First Nations Australians being adversely affected by social, cultural and commercial determinants of health stemming from colonisation, racism and remoteness.3 Chronic liver disease is the key cause of HCC and most chronic liver disease is preventable and treatable.4,5 First Nations Australians shoulder a disproportionate burden of chronic liver disease (Box 1).6 Alcoholrelated liver disease is a leading cause of HCC in all Australians, including First Nations Australians.2,7,8 The prevalence of hepatitis B and C is two-to three-fold higher in First Nations Australians compared with non-Indigenous Australians.2,5 The most prevalent hepatitis B genotype in remote First Nations communities (genotype C4) is associated with more aggressive liver disease and increased HCC risk compared with other genotypes.9,10 Obesity and type 2 diabetes, both leading risk factors for metabolic-associated fatty liver disease, are twice as common in First Nations Australians than in non-Indigenous Australians.3 Importantly, First Nations Australians are more likely to have multiple cofactors driving liver injury, 2 warranting a multipronged approach to HCC prevention. MethodsThe two lead authors of this article, one First Nations Australian and one non-Indigenous Australian, established a nationally representative, diverse group of four First Nations Australian and 14 non-Indigenous Australian clinical and research leaders in the fields of HCC and chronic liver disease for the project.All authors have expertise in the provision of regional or remote models of HCC or chronic liver disease care, or both.First, an initial two-hour virtual meeting was held, where authors shared their thoughts and responses to two main topics: i) identifying unmet needs in prevention, diagnosis and treatment of HCC in First Nations Australians; and ii) identifying opportunities to address these unmet needs.All authors shared key evidence and their experiences and perspectives, representing the differing epidemiology, health resourcing, policy and legislative contexts across all Australian states and territories.From this discussion, the lead authors developed a list of eight key action items using a positive, evidencebased approach that prioritised First Nations-led interventions and models of care.The aim was to retain action items that had 100% agreement across the author group; all eight action items were ratified by all authors.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".