Cancerpedia: A Framework for Implementing Comprehensive Cancer Centres
Bibliographic record
Abstract
Background and context: Comprehensive cancer centers or programs form a nucleus of cancer care delivery. Although there are frameworks for population cancer control, no similar published framework exists for cancer centers. Aim: We sought to develop a framework for designing and implementing a comprehensive cancer center or program within the context of a population-based model of cancer control that spans diagnosis, treatment, supportive care, and palliative care as well as integration with primary care and the community. Strategy/Tactics: The framework was constructed with the patient at the center and provides a system-level perspective as well as a granular view of the fundamental resources and structures needed to build and maintain individual cancer centers and programs. Due to its breadth, we focused the framework on essential information while linking to a wide range of vetted publications that detail additional standards, guidelines and best practices. Program/Policy process: “Cancerpedia” emerged as a cohesive framework for the delivery of high-quality cancer care within and beyond the cancer center. It provides an overview of the cancer control and care delivery framework, describes cancer care services (e.g., radiotherapy, chemotherapy, palliative care) and details infrastructure and core services (e.g., physical facilities, human resources). In addition to these services, the framework presents guidelines for governance that ensure oversight and quality, describes the critical need for integrating education and research and presents the best practices for engaging in philanthropy. Cancerpedia also outlines the role of the comprehensive cancer center in integration with the community and influencing policy and regulation. Over 30 chapters provide a detailed description of each element and include a description of the service or function, resources requirements such as people, equipment and facilities, management structures, quality performance guidelines and future trends in innovation. Outcomes: To our knowledge, no comparable published framework exists as a reference for developing comprehensive cancer centers. Cancerpedia was designed to serve as a global public good and is adaptable and applicable to diverse contexts and healthcare environments. It is relevant to high-, middle- and low-income countries alike and provides a reference point from which to structure a plan for growth. What was learned: While it is important to describe the various elements required for cancer care delivery, it is critical to consider and address the integration and interdependencies of these various elements. Future opportunities for learning include seeking input from a global audience to gauge the utility and applicability of Cancerperdia to local contexts.
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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 | 0.066 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".