Intellectual changes after radiation for children with brain tumors: which brain structures are most important?
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate the contribution of radiation dose to different intracranial structures on changes in intellectual function for children with brain tumors. METHODS: We evaluated children with brain tumors treated in 2005-2017 who had longitudinal neuropsychological assessments and available photon dosimetric data (if radiation therapy [RT] given). Full Scale Intelligence Quotient (FSIQ) and index scores were evaluated (perceptual reasoning index [PRI], processing speed index [PSI], verbal comprehension index [VCI], and working memory index [WMI]). Multivariable linear mixed effects models were used to model endpoints, with age at RT and dose to different brain regions as fixed effects and patient-specific random intercepts. P-values (P*) were adjusted for multiple comparisons. RESULTS: Sixty-nine patients were included, 56 of whom received RT. Median neuropsychological follow-up was 3.2 years. Right temporal lobe mean dose was strongly associated with decline in FSIQ (P* = 0.005); with each gray increase in mean dose, there was a decrease of 0.052 FSIQ points per year. Dose to 50% (D50) of the supratentorial brain was associated with decline in PSI (P* = 0.006) and WMI (P* = 0.001). Right and left hippocampus D50 were individually strongly associated with declines in VCI (P* = 0.009 for each). Presence of a ventriculoperitoneal shunt decreased FSIQ by 10 points. CONCLUSIONS: We reported associations between dosimetry to specific brain regions and intellectual outcomes, with suggested avoidance structures during RT planning. These models can help clinicians anticipate changes in neurocognition post-RT and guide selection of an optimal RT plan.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".