Predicting changes in adaptive functioning and behavioral adjustment following treatment for a pediatric brain tumor: A report from the Brain Radiation Investigative Study Consortium
Bibliographic record
Abstract
BACKGROUND: Children are at risk for behavioral and adaptive difficulties following pediatric brain tumor. This study explored whether familial/demographic, developmental, diagnostic, or treatment-related variables best predict posttreatment behavioral and adaptive functioning. METHODS: Participants included 40 children (mean age = 12.76 years, SD = 4.01) posttreatment (mean time since diagnosis = 1.99 years, SD = 0.21) for pediatric brain tumor. Parents rated children's behavioral adjustment and adaptive functioning and provided demographic and developmental histories. Diagnostic and treatment-related information was abstracted from medical records. RESULTS: Ratings of adaptive and behavioral functioning approximately 2 years postdiagnosis were within the average range, although the percentage of children exceeding clinical cutoffs for impairment in adaptive skills exceeded expectation, particularly practical skills. Premorbid behavior problems and tumor size predicted posttreatment adaptive functioning. After accounting for adaptive functioning near diagnosis, premorbid behavior problems predicted declines in adaptive functioning 2 years postdiagnosis. After accounting for adjustment near diagnosis, no variables predicted declines in behavioral adjustment. CONCLUSIONS: Children may be vulnerable to reduced adaptive functioning following pediatric brain tumor treatment, especially in practical skills. Assessing prediagnosis functioning and diagnostic and treatment-related variables may improve our ability to predict those at greatest risk, although those factors may be less helpful in identifying children likely to develop behavioral difficulties. Screening of these factors in tertiary care and long-term follow-up settings may improve identification of those at greatest need for support services.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".