The Requirements for Setting Up a Dedicated Structure for Adolescents and Young Adults with Cancer—A Systematic Review
Bibliographic record
Abstract
Adolescents and young adults (AYAs), often defined as those aged 15-39 years, face unique challenges in oncology that are often unmet by conventional care models. This systematic review examines evidence on establishing dedicated AYA oncology units, focusing on logistical, infrastructural, and personnel-related recommendations. A PRISMA-guided search of PubMed (2000-2024) identified seven studies that emphasized early stakeholder involvement and collaboration between pediatric and adult oncology teams to ensure comprehensive care. Multidisciplinary teams (MDTs) of oncologists, nurses, and psychosocial support staff were highlighted as essential to address AYA patients' diverse needs. Care models varied, with some advocating consultation-based services and others supporting dedicated units. Priorities included increasing clinical trial enrollment, fertility counseling, and creating environments attuned to AYA patients' social and psychological needs. Key barriers included limited funding, institutional resistance, and inadequate pediatric/adult team collaboration. Despite progress, the lack of standardized guidelines and long-term data on AYA unit efficacy remains a challenge. Further research is required to develop outcome metrics, refine care models, and enhance survival and quality of life for AYA cancer patients.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".