Risk-Stratified Pathways for Cancer Survivorship Care: Insights from a Deliberative Multi-Stakeholder Consultation
Bibliographic record
Abstract
Risk-stratified pathways of survivorship care seek to optimize coordination between cancer specialists and primary care physicians based on the whole person needs of the individual. While the principle is supported by leading cancer institutions, translating knowledge to practice confronts a lack of clarity about the meaning of risk stratification, uncertainties around the expectations the model holds for different actors, and health system structures that impede communication and coordination across the care continuum. These barriers must be better understood and addressed to pave the way for future implementation. Recognizing that an innovation is more likely to be adopted when user experience is incorporated into the planning process, a deliberative consultation was held as a preliminary step to developing a pilot project of risk-stratified pathways for patients transitioning from specialized oncology teams to primary care providers. This article presents findings from the deliberative consultation that sought to understand the perspectives of cancer specialists, primary care physicians, oncology nurses, allied professionals, cancer survivors and researchers regarding the following questions: what does a risk stratified model of cancer survivorship care mean to care providers and users? What are the prerequisites for translating risk stratification into practice? What challenges are involved in establishing these prerequisites? The multi-stakeholder consultation provides empirical data to guide actions that support the development of risk-stratified pathways to coordinate survivorship care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".