Academic readiness among young children treated for brain tumors: a multisite, prospective, longitudinal trial
Bibliographic record
Abstract
BACKGROUND: Young children treated for central nervous system (CNS) malignancies are at high risk for difficulties with academic functioning due to increased vulnerability of the developing brain and missed early developmental opportunities. Extant literature examining academics in this population is limited. We investigated academic readiness, its clinical and demographic predictors, and its relationship with distal academic outcomes among patients treated for CNS tumors during early childhood. METHODS: Seventy patients with newly diagnosed CNS tumors were treated on a prospective, longitudinal, multisite study with chemotherapy, with or without photon or proton irradiation. Patients underwent assessments of academic skills at baseline, 6 months, 1 year, and then annually for 5 years. Assessments measured academic readiness and academic achievement in reading and math. RESULTS: Mixed linear models revealed slowed development of academic readiness skills over time. Socioeconomic status (SES) was predictive of academic readiness at all time points. Other demographic (eg, age at treatment) and clinical (eg, shunt status, treatment exposure) variables were not predictive of academic readiness. Distal reading difficulties were proportionally greater than normative expectations while math difficulties did not differ. Academic readiness was predictive of distal academic outcomes in reading and math. CONCLUSIONS: Treatment for CNS malignancies in early childhood appears to slow development of academic readiness skills, with SES predictive of risk. Academic readiness skills were predictive of subsequent academic achievement. A disproportionate number of long-term survivors performed below age-based expectations in reading. These findings suggest the need for monitoring and interventions targeting early academic skills in this population.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".