OP69 Initiatives To Improve The Timeliness Of Cancer Diagnosis
Bibliographic record
Abstract
Introduction Conventional wisdom suggests that accelerating the speed of cancer diagnosis should improve health outcomes. However, cancer diagnosis requires complex coordination and effective communication between care providers working across many areas of the healthcare system. Since 2000, several nations and jurisdictions have aimed to improve timeliness of cancer diagnosis by integrating and coordinating cancer diagnostic services for patients. The objective of this study was to describe the impact of these existing initiatives. Methods We conducted an environmental scan consisting of a literature review (published academic and grey literature) and key informant consultations (online surveys and telephone interviews with experts who have knowledge of existing initiatives). We searched for initiatives in the United Kingdom, the Nordic countries, Canada, Australia, and New Zealand. For each initiative, we extracted data on their development and implementation, structure and functioning, intended outcomes and effectiveness, costs and cost savings, and enablers and barriers. Results Eighty-nine relevant documents and 20 key informants contributed to this study. We identified 21 relevant initiatives, including seven national initiatives targeting multiple types of cancer. The literature review found that most initiatives accelerated the diagnostic phase of cancer care by several days or weeks. These wait time reductions were often associated with improved patient experience, but not less advanced cancer stage or increased long-term survival. Insights from key informants improved our understanding of the costs, enablers, and barriers associated with program implementation and maintenance. Conclusions These results can be used as a first step to inform the development, evaluation, and improvement of international cancer diagnostic pathways. Stakeholders wishing to accelerate cancer diagnosis should consider the feasibility of achieving their intended program outcomes based on the existing research evidence, desired type of initiative, and jurisdiction's unique contextual factors.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".