Interventions to improve neurocognitive late-effects in pediatric and adolescent CNS tumor patients and survivors - a systematic review
Bibliographic record
Abstract
Introduction: Survival of children and adolescents diagnosed with central nervous system (CNS) tumors massively improved over the last decades due to better diagnostics, treatment, and supportive care. However, morbidity is still the highest of all cancer entities in this age group with neurocognitive late-effects being one of the most severe. Aim: With this systematic review, we aim to summarize interventions designed to prevent or improve neurocognitive late-effects in CNS tumor patients. Method: 2022 and included publications studying interventions for neurocognitive late-effects in pediatric and adolescent patients and survivors diagnosed with a CNS tumor. We included any form of neurocognitive intervention during treatment or following treatment completion. We considered all types of studies except for expert opinions and case reports. Results: The literature search resulted in 735 publications. We included 43 publications in the full text screening and 14 met our inclusion criteria. Of those, two assessed the impact of pharmacological interventions, three of exercise interventions, five of online cognitive training, and four assessed behavioral interventions. Different neuropsychological test batteries and imaging were used to measure the impact of the respective interventions. Most studies showed a positive impact of the interventions in single to several of the subtests used. Conclusion: We found several intervention studies indicating improvement of neurocognitive problems in children and adolescent CNS tumor survivors. In this population exercise interventions or online cognitive training might mitigate or improve neurocognitive late-effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".