Processing speed in children treated for brain tumors: effects of radiation therapy and age
Bibliographic record
Abstract
The current study examined processing speed in children two years post-treatment for brain tumors (BT) with radiation therapy (RT) compared to those treated with without RT. Participants included 59 children (4-17 years) with BT assessed as part of the Brain Radiation Investigative Study Consortium (BRISC). Processing speed was assessed at two time points: Time1 (3-9 months post-surgery) for 26 children who received whole brain or focal RT (RT group) and 33 treated without RT (no-RT group), and again two years later (Time2) for 42 participants (17 RT, 25 no-RT). Linear mixed effects (LME) regression analyses examined differences in cognitive and motor speed between groups and across visits, with age at Time1 (age1) treated as a moderating variable, and sex and primary tumor size as covariates. No effects for treatment group or visit were found for motor speed (Pegboard) or mean reaction time (Attention Network Task). On the Wechsler Processing Speed Index (PSI), the no-RT group performed better than the RT group, with a group-by-age interaction such that across visits, the difference between the no-RT and RT groups was larger among children who were older at initial treatment (≥10 years) than among those who were younger (<10 years). Cumulative brain injury earlier in life (tumor, surgery, plus RT) may result in greater impact on more complex tasks of cognitive efficiency. Children receiving RT showed reduced processing speed over time, with a larger group difference among those who were over 10 years at treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".