NCOG-11. Factors influencing social competence in adolescents and young adults surviving pediatric brain tumors treated with radiation therapy: A scoping review
Bibliographic record
Abstract
Abstract Pediatric brain tumors (PBTs) are among the most prevalent childhood cancers, with survivors often experiencing significant cognitive and social challenges, particularly those who undergo cranial irradiation. Despite increasing use of cognitive assessments in clinical practice and research, social deficits, critical during the formative years of Adolescents and Young Adults (AYA), are frequently overlooked. Social competence, defined as the ability to assess, interact with, and adapt to social environments, is often impaired in this population yet remains underexplored. This scoping review investigates the factors influencing social competence in AYA survivors of PBTs, particularly those treated with radiation therapy, and outlines best practices for measurement and intervention in clinical settings. A scoping review was conducted using PUBMED to identify randomized controlled trials, clinical trials, and qualitative or quantitative studies published between 2009 and 2024. Eligible studies focused on social competence in participants aged 0–40 years, excluding reviews, studies without a focus on social competence, and participants over 40. Twelve studies identified significant associations between infratentorial tumors, cranial radiation therapy (CRT), large tumor size, and persistent social and cognitive deficits. Challenges with cooperation, self-control, and emotional regulation were commonly reported and often persisted for years post-treatment. Proton radiotherapy (PRT) demonstrated reduced cognitive side effects compared to photon radiotherapy (XRT); however, neither modality spared survivors from social competence deficits. Effective assessment tools included the Social Skills Rating System (SSRS) and the Child Behavior Checklist (CBCL), though reliance on proxy reporting limited nuanced insights. Structured social skills training programs showed promise, with improvements sustained for months post-intervention. This review highlights the enduring social challenges faced by AYA survivors of PBTs and emphasizes the urgent need for evidence-based interventions to address these deficits. Incorporating social competence assessment and targeted interventions into clinical practice and trials is essential to enhance long-term quality of life for survivors.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".