Abstract A008: Psychosocial Survivorship Gaps in Early-Onset Cancers: Opportunities for Digital Health Research
Bibliographic record
Abstract
Abstract Background: The incidence of cancers diagnosed before age 50 has risen steadily over the past several decades. Survivorship research has not advanced at the same pace as these trends. Existing models of care were developed primarily for older adults and emphasize medical surveillance and risk reduction. Psychosocial dimensions, including anxiety, fear of recurrence, uncertainty about long-term health, and difficulty returning to social or professional roles, remain less consistently addressed. These needs are increasingly relevant as more individuals face the long-term impact of early-onset cancers. Methods: We are developing a digital survivorship model designed to extend support beyond the clinic and focus on psychosocial recovery in the early post-treatment period. The intervention integrates evidence-based behavioral strategies with participatory input from survivors, providers, and community partners. Features under development include educational modules, guided reflection, and interactive prompts to strengthen coping, resilience, and self-management. Evaluation will examine feasibility, acceptability, and early signals of impact using surveys, usage metrics, and qualitative interviews. Preliminary Results: Stakeholder engagement to date has demonstrated strong demand for resources that are both accessible and relevant to the life stage of early-onset survivors. Survivors report that many existing programs feel generic or better suited to older adults. Key priorities identified include managing fear of recurrence, navigating uncertainty about long-term effects, and balancing recovery with the responsibilities of family, career, and caregiving. These themes are informing design decisions and shaping evaluation measures. Conclusion: The rise in early-onset cancers creates an opportunity to expand the scope of cancer control to include survivorship as a central focus. Addressing psychosocial outcomes such as coping, resilience, and quality of life is essential for long-term health. Digital health strategies provide a scalable way to meet these needs by delivering tailored support that complements advances in prevention, detection, and treatment. Integrating survivorship into the broader response to early-onset cancers ensures progress is measured not only by survival but also by the ability of survivors to live well after treatment. Citation Format: Manisha Salinas. Psychosocial Survivorship Gaps in Early-Onset Cancers: Opportunities for Digital Health Research [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A008.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".