Outcome for Australian children with acute leukaemia is influenced by ethnicity and the metro-regional divide
Bibliographic record
Abstract
Objective: To compare disease presentation and outcome in Aboriginal and non-Aboriginal children with acute leukaemia and to assess the impact of remoteness and area-based socioeconomic disadvantage. Design: A retrospective review of children treated for acute leukaemia from South Australia (SA), Northern Territory (NT) and Western Australia (WA) between 2009 and 2018. Setting: Women’s and Children’s Hospital (WCH), Adelaide and Perth Children’s Hospital (PCH) - the sole referral sites for paediatric cancer for SA, NT and WA. Participants: Eligible patients, aged between 1 day and <18 years, diagnosed with acute leukaemia. Main Outcome Measures: Leukaemia diagnosis and overall survival. Results: Analysis of 455 children treated for acute leukaemia showed inferior survival outcomes were associated with remote/very remote localities (p=0.004). Five-year overall survival was 91.7% (95% CI: 87.9%-94.3%) for children with ALL and 69.8% (56.7%-79.5%) for AML. A large percentage of Aboriginal children from SA/NT were diagnosed with AML compared to others (60.0% vs. 14.4%, p=0.001). A trend towards inferior overall survival was seen for Aboriginal children with ALL compared to non-Aboriginal children (82.4% vs. 92.2%, p=0.07) and 55.6% were high-risk by study criteria; however, MRD testing did not identify any high MRD cases. Aboriginal children were less likely to be enrolled on clinical trials (34.5% vs. 53.1%, p=0.03) and more likely to be lost to follow-up (41.4% vs. 13.2%, p<0.001). Conclusion: Aboriginal ethnicity and geographic remoteness of residence are adverse prognostic factors for Australian children with leukaemia. Additional strategies are required to ensure improvements in follow-up and survival of these children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".