Risk Stratification and Cancer Follow-Up: Towards More Personalized Post-Treatment Care in Canada
Bibliographic record
Abstract
After treatment, cancer survivors require ongoing, comprehensive care to improve quality of life, reduce disability, limit complications, and restore function. In Canada and internationally, follow-up care continues to be delivered most often by oncologists in institution-based settings. There is extensive evidence to demonstrate that this model of care does not work well for many survivors or our cancer systems. Randomized controlled trials have clearly demonstrated that alternate approaches to follow-up care are equivalent to oncologist-led follow-up in terms of patient outcomes, such as recurrence, survival, and quality of life in a number of common cancers. In this paper, we discuss the state of follow-up care for survivors of prevalent cancers and the need for more personalized models of follow-up. Indeed, there is no one-size-fits-all solution to post-treatment follow-up care, and more personalized approaches to follow-up that are based on individual risks and needs after cancer treatment are warranted. Canada lags behind when it comes to personalizing follow-up care for cancer survivors. There are many reasons for this, including difficulty in determining who is best served by different follow-up pathways, a paucity of evidence-informed self-management education and supports for most survivors, poorly developed IT solutions and systems, and uneven coordination of care. Using implementation science theories, approaches, and methods may help in addressing these challenges and delineating what might work best in particular settings and circumstances.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".