Equitable Access to Genomic Molecular Testing for Australian Cancer Patients: Insights from the Victorian Precision Oncology Summit
Bibliographic record
Abstract
The Victorian Precision Oncology Summit, convened in 2023, was a joint initiative between the Victorian Comprehensive Cancer Centre Alliance (VCCC Alliance) and the Monash Partners Comprehensive Cancer Consortium (MPCCC) and was proposed to guide a coordinated state-wide conversation about how the oncology sector can overcome some of the current obstacles in achieving equity of access to clinical cancer genomics for Victorian patients. Themes that emerged from discussion groups at the Summit include standardisation, centralisation, funding, education and communication and insights across those themes are outlined in this manuscript. The event served as a large consultation piece for the development of a broader precision oncology roadmap, which explores equitable access to molecular testing for Victorian patients, currently in development by the VCCC Alliance and MPCCC in collaboration with other key Victorian and national stakeholders. While this symposium was a Victorian initiative, it is felt that the insights garnered from this consultation piece will be of interest to consumer groups, clinicians, researchers, educators, policy makers and other key stakeholders in other states of Australia as well as in other countries implementing comprehensive genomic profiling within complex health systems.
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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 | 0.043 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".