Long-Term Follow-Up of Pediatric CNS Tumor Survivors—A Selection of Relevant Long-Term Issues
Bibliographic record
Abstract
Introduction: Survivors of pediatric central nervous system (CNS) tumors are at high risk for late effects and long-term morbidity. The quality of survival became increasingly important, as advances in diagnostics, multimodal treatment strategies, and supportive care have led to significant increases in long-term survival. Aim: This review aims to provide a global overview of the potential late effects and long-term follow-up care of CNS tumor survivors, directed to trainees and practitioners with less targeted training in pediatric oncology. Late effects in CNS tumor survivors: A specific focus on CNS tumor survivors relies on cognitive and psychosocial late effects, as they may have an impact on education, professional career, independent living, and quality of life. Further important late effects in CNS tumor survivors include endocrine, metabolic, cardiovascular, and cerebrovascular diseases. Conclusions: Comprehensive long-term follow-up care is essential for pediatric CNS tumor survivors to improve their quality of survival and quality of life. An individualized approach, taking all potential late effects into account, and carried out by an interdisciplinary team, is recommended, and should continue into adulthood. Existing recommendations and guidelines on long-term follow-up care guide the multidisciplinary teams.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".