Unveiling a Health Disparity: Comparative Analysis of Head and Neck Cancer Trends between First Nations People and Non-Indigenous Australians (1998–2015)
Bibliographic record
Abstract
Background: We aim to assess and compare the HNC trends between the First Nations and non-Indigenous population. Methods: HNC incidence (1998–2013) and mortality (1998–2015) data in First Nations people and non-Indigenous Australians were utilised from the Australian Cancer Database. The age-standardised incidence and mortality trends along with annual percentage changes were analysed using Joinpoint models. Age-standardised incidence and mortality rates according to remoteness, states, and five-year survival rates among First Nations people and non-Indigenous Australians were presented as graphs. Results: First Nations people had over twice the age-standardised incidence (2013; 29.8/100,000 vs. 14.7/100,000) and over 3.5 times the age-standardised mortality rates (2015; 14.2/100,000 vs. 4.1/100,000) than their non-Indigenous counterparts. Both populations saw a decline in mortality, but the decline was only statistically significant in non-Indigenous Australians (17.1% decline, 1998: 4.8/100,000, 2015: 4.1/100,000; p < 0.05). Across all remoteness levels and states, First Nations people consistently had higher age-standardised incidence and mortality rates. Furthermore, the five-year survival rate was lower by 25% in First Nations people. Conclusion: First Nations people continue to shoulder a disproportionate HNC burden compared to non-Indigenous Australians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".