Mapping models of care for children, adolescent and young adult cancer survivors: A scoping review.
Bibliographic record
Abstract
e13793 Background: Young cancer survivors, with decades of life ahead, encounter substantial risks for treatment-related late effects. Achieving optimal outcomes requires a comprehensive multi-dimensional approach. This scoping review aimed to systematically map the evidence on models of survivorship care for Child (0-14) and Adolescent and Young Adult (AYA, 15-39) cancer survivors aligning with the Framework for Quality Cancer Survivorship. Methods: Conducted according to PRISMA extension for scoping reviews, this study systematically searched MEDLINE, CINAHL and Embase for English articles from Jan 2006 to Jan 2024. Inclusion criteria were studies describing patient and health service domain for Child and AYA cancer survivorship models of care. Dual processes were used for article screening, selection, and data extraction. A deductive analysis, using Content Analysis informed results, presented in a narrative synthesis. Results: Of the 5,298 records screened 38 described Child and 10 described AYA models of care. The majority were cohort and cross-sectional studies from the USA, Canada, and the UK representing 6,101 Child and 5,283 AYA cancer survivors. Across these diverse studies, a broad range of outcomes were identified with most domains of the Framework represented. No model of care described more domains than another. Across the 13 possible elements of care and outcomes, most (68%) reported on less than five of these. The domains most represented included elements of care rather than outcomes at the service level such as the clinic structure (52%), communication and decision making (52%) and patient/carer satisfaction (50%). At the patient level, quality of life outcomes and assessment and management of physical symptoms were represented in 40% of studies. There were few studies reporting on health promotion (23%), chronic conditions (13%), mortality and healthcare utilization (5% each) and none reported on costs. In terms of outcomes, oncologist long-term follow up care showed higher adherence to risk-based recommendations compared to primary care models in some studies. Other studies suggest a shared care model increases adherence and decrease lost to follow-up. At the service level, high satisfaction with care was common in all models. Overall, there was not enough to evidence to support one model over another in either Child or AYA survivorship care. Conclusions: Despite decades of research in survivorship care, there is not enough evidence about the effectiveness and efficiency of different models of care. No single model stands out as superior, and there are notable gaps in understanding the economic and value proposition of different models. Addressing these gaps is crucial for the future development, and provision of quality survivorship care tailored for this patient population.
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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 | 0.028 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".