“If you build it, they will come”:the convergence of funding, research and collaboration in paediatric brain cancer clinical trials
Bibliographic record
Abstract
Each year, approximately 1000 children in Australia and New Zealand, aged 0–14 years, are diagnosed with cancer. Despite paediatric cancer accounting for less than 1% of all cancer cases, the impact on their families and communities is profound and disproportionate.1-3 Paediatric brain cancers are the most significant cause of cancer-related deaths within this age group, responsible for 40% of fatalities despite representing only 14% of diagnoses.2 Although significant advances in paediatric cancer treatments have pushed overall cure rates above 80%, the outlook for many brain tumour types remains bleak.4 Moreover, survivors often face lifelong clinical sequelae that severely diminish their quality of life,5 with 60% of survivors unable to reach independence in adulthood.6 This stark reality underscores the need for the expansion of clinical trials and integrated preclinical research aimed at improving outcomes for these individuals.
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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 | 0.365 | 0.439 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".