Integrated Cancer System: a perspective on developing an integrated system for cancer services in London
Bibliographic record
Abstract
This article explores the potential for integrated cancer systems to improve the quality of care and deliver cost efficiencies and improve outcomes for cancer patients. Currently, patients in the UK still have poorer survival rates than comparable countries such as Canada, Sweden, Norway and Australia. Improving the quality of cancer services is a key policy objective and cancer is a priority outcome measure in both the NHS and Public Health Outcomes Framework. Evidence suggests that better integrated delivery has the potential to improve the quality and reduce the cost of healthcare, and ultimately improve health outcomes. One of the key themes from the Model of Care for Cancer Services (1) was that cancer services should be commissioned along pathways and that provider networks should be established to deliver care. London has two integrated cancer systems; one covering north central and east London (London Cancer) and the other covering west and south London (London Cancer Alliance). There a number of areas in cancer care that the current model of service provision has failed to adequately address and which have the potential to improve significantly though implementation of integrated services. These include improving early diagnosis, reducing inequalities in access to treatment and outcomes and maximising research and training across the system. Important drivers for the integration of cancer services are strong clinical leadership, shared informatics systems, focusing on quality of services and improving patient experience. Emerging needs of integrated cancer in London are around strengthening the involvement of primary care, public health and the third sector; working to develop sufficient capacity and expertise in primary care and collaborating more closely with commissioners to develop integrated 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".