Australia and New Zealand's responsibilities in improving oncology services in the Asia‐Pacific: A call to action
Bibliographic record
Abstract
AIM: To review the expected increasing demand for cancer services among low and middle-income countries (LMICs) in the Asia-Pacific (APAC), and to describe ways in which Australia and New Zealand (ANZ) can provide support to improve cancer outcomes in our region. METHODS: We first review the current and projected incidence of cancer within the APAC between 2018 and 2040, and the estimated demand for chemotherapy, radiotherapy and surgery. We then explore potential ways in which ANZ can increase regional collaborations to improve cancer outcomes. RESULTS: We identify 6 ways that ANZ can collaborate with LMICs to improve cancer care in the APAC through the ANZ Regional Oncology Collaboration Strategy: Increasing education and institutional collaborations in the APAC region through in-country training, twinning partnerships, observerships and formalised training programs in order to increase cancer care quality and capacity. Promoting and assisting in the establishment and maintenance of population-based cancer registries in LMICs. Increasing research capacity in LMICs through collaboration and promoting high quality global oncology research within ANZ. Engaging and training Australian and New Zealand clinicians in global oncology, increasing awareness of this important career path, and increasing health policy engagement. Increasing web-based endeavours through virtual tumour boards, web-based advocacy platforms and web-based teaching programs. Continuing to leverage for funding through professional bodies, government, industry, not-for-profit organisations and local hospital funds. CONCLUSION: We propose the creation of an Australian and New Zealand Interest Group to provide formalised and sustained collaboration between researchers, clinicians and stakeholders.
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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 | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.017 | 0.019 |
| 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".