Young Adult Cancer Survivorship: Recommendations for Patient Follow-up, Exercise Therapy, and Research
Bibliographic record
Abstract
Survivors of adolescent and young adult cancers (AYAs) often live 50 to 60 years beyond their diagnosis. This rapidly growing cohort is at increased risk for cancer- and treatment-related 'late effects' that persist for decades into survivorship. Recognition of similar issues in pediatric cancer survivors has prompted the development of evidence-based guidelines for late effects screening and care. However, corresponding evidence-based guidelines for AYAs have not been developed. We hosted an AYA survivorship symposium for a large group of multidisciplinary AYA stakeholders (approximately 200 were in attendance) at Princess Margaret Cancer Centre (Toronto, Ontario, Canada) to begin addressing this disparity. The following overview briefly summarizes and discusses the symposium's stakeholder-identified high-priority targets for late effects screening and care and highlights knowledge gaps to direct future research in the field of AYA survivorship. This overview, although not exhaustive, is intended to stimulate clinicians to consider these high-priority screening and care targets when seeing survivors in clinical settings and, ultimately, to support the development of evidence-based late effects screening and care guidelines for AYAs.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".