Neurocognitive and Emotional Outcomes in Childhood Cancer: A Developmental Perspective
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors (CCSs) are at heightened risk of long-term neurocognitive and emotional difficulties that can affect educational attainment, social participation, and overall quality of life. These outcomes vary across developmental stages and are influenced by treatment modality, age at diagnosis, and central nervous system (CNS) involvement. METHODS: A comprehensive literature search was conducted in PubMed, Scopus, PsycINFO, and Web of Science for articles published between January 2000 and June 2024. Search terms included combinations of "childhood cancer survivors," "neurocognitive outcomes," "executive function," "emotional regulation," and related MeSH terms. Inclusion criteria required peer-reviewed studies assessing CCS using standardized neuropsychological or emotional measures. RESULTS: Evidence indicates persistent deficits in processing speed, working memory, and higher-order executive functions, with additional challenges in attention and memory. Emotional difficulties, including anxiety, depression, and social withdrawal, were prevalent and often co-occurred with cognitive impairments. Developmental timing of cancer and treatment was a key determinant of outcome. Family functioning, school reintegration support, and broader social environments emerged as important moderators of resilience. CONCLUSIONS: CCSs face complex, interrelated cognitive and emotional challenges that warrant early identification and ongoing, developmentally tailored intervention. Integrated approaches combining cognitive remediation and psychosocial support appear most effective. Future research should prioritize longitudinal designs, multi-informant assessments, and culturally sensitive frameworks to inform targeted prevention and rehabilitation strategies. Our synthesis highlights that deficits in processing speed and working memory are most pronounced following CNS-directed therapies during early developmental stages, whereas emotional vulnerabilities such as anxiety and social withdrawal often emerge later in adolescence. Interventions combining cognitive remediation, targeted psychosocial support, and structured school reintegration show the strongest evidence for improving adaptive outcomes. Coordinated survivorship care across healthcare, educational, and family systems is essential to sustain developmental recovery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".