Quantifying the Diagnosis and Survival of Early Onset Bowel Cancer Among First Nations Peoples in Queensland, Australia
Bibliographic record
Abstract
INTRODUCTION: The incidence of early-onset bowel cancer (EOBC) is increasing in Australia and globally. However, the burden of EOBC among First Nations Australians is rarely determined. This study aimed to quantify the diagnosis and survival rates of EOBC among First Nations Peoples in Queensland, Australia. METHODS: CancerCostMod, a linked administrative dataset of patients diagnosed with cancer in Queensland from 1st July 2011 to 30th June 2015, was used. EOBC was defined as a diagnosis of bowel cancer (i.e., colon, rectosigmoid, or rectal cancer) at 18-49 years of age. A multivariable logistic regression analysis was employed to determine the association of Indigenous status and other factors with a diagnosis of EOBC. Five-year survival rates were used to estimate the survival rate. RESULTS: Of 11,702 bowel cancer cases, 9.2% (95% CI: 8.7%-9.7%) were EOBC, with 19% among First Nations peoples and 9% among Non-First Nations. First Nations Australians had 2.6 times the odds of EOBC diagnosis (95% CI: 1.7-4.0) compared with Non-First Nations Australians. Overall, EOBC patients showed a significantly higher 5-year survival rate of 77% compared with 60% for late-onset bowel cancer patients. However, First Nations EOBC patients showed a lower 5-year survival rate (73%) than Non-First Nations EOBC patients (77%). CONCLUSION: First Nations Australians have more than double the diagnosis rates and lower 5-year survival for EOBC compared to Non-First Nations. Whilst the recent lowering of the age eligibility for the National Bowel Cancer Screening Program is a beneficial strategy to address the increasing incidence of EOBC, special consideration should be given to addressing the higher diagnosis rates and lower survival among First Nations Australians. This study raises the potential for further lowering the age eligibility for First Nations Australians to ensure younger First Nations Australians can access screening for earlier detection, thereby improving their survival from bowel cancer.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".