Development of cancer genetic services in the UK: A national consultation
Bibliographic record
Abstract
BACKGROUND: Technological advances in DNA sequencing have made gene testing fast and affordable, but there are challenges to the translation of these improvements for patient benefit. The Mainstreaming Cancer Genetics (MCG) programme is exploiting advances in DNA sequencing to develop the infrastructure, processes and capabilities required for cancer gene testing to become routinely available to all those that can benefit. METHODS: The MCG programme held a consultation day to discuss the development of cancer genetics with senior representation from all 24 UK cancer genetic centres. The current service landscape and capacity for expansion was assessed through structured questionnaires. Workshop discussion addressed the opportunities and challenges to increasing cancer gene testing in the National Health Service (NHS). RESULTS: Services vary with respect to population served and models of service delivery, and with respect to methods and thresholds for determining risk and testing eligibility. Almost all centres want to offer more cancer gene testing (82%) and reported increasing demand for testing from non-genetic clinical colleagues (92%). Reported challenges to increasing testing include the complexity of interpreting the resulting genetic data (79%), the level of funding and complexity of commissioning (67%), the limited capacity of current processes and cross-disciplinary relationships (38%), and workforce education (29%). CONCLUSIONS: Priorities to address include the development and evaluation of models of increasing access to gene testing, the optimal process for interpretation of large-scale genetic data, implementation of appropriate commissioning and funding processes, and achieving national consistency. The UK cancer genetics community have high expertise and strong commitment to maximising scientific advances for improved patient benefit and should be pivotally involved in the implementation of increased cancer gene testing.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 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".