An Integrated, Population-Based Framework for Knowledge Management for Cancer Control
Bibliographic record
Abstract
Cancer control organizations commonly refer to the critical role of clinical practice guidelines to support the best possible cancer care. But how can a cancer care program ensure the systematic implementation of those guidelines? The goals of this article are to describe the process of developing a cancer control system driven by knowledge management, to highlight the key elements of this system and to foster discussion on the implementation of such frameworks. In order to promote best cancer practices within an expanded radiation service model for the province of Alberta, we developed an integrated conceptual framework for knowledge management. We identified six key elements of a knowledge management framework for the cancer program: evidence-based provincial guidelines, funding decisions, harmonized care pathways, targeted knowledge transfer projects, performance measurement and feedback to the system. We are establishing a process to characterize the explicit linkages and accountabilities between each of these elements as part of a broader cancer care quality agenda. We will implement the framework to support the start-up of the first of three new radiation treatment services in the province. The basic elements of a guidelines-supported cancer care system are not in doubt; how to unambiguously engage them within an integrated care system remains an area of intense interest.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".