Premorbid functioning as a predictor of outcome in pediatric brain tumor: An initial examination of the normalcy assumption
Bibliographic record
Abstract
BACKGROUND: Research on neurodevelopmental outcome in survivors of pediatric brain tumor (BT) is often based on the assumption of normal development up to the onset of overt symptoms. We sought to verify the "normalcy assumption" and to investigate corollary issues including challenges inherent to the measurement of premorbid neurobehavioral functioning. PROCEDURE: The Brain Radiation Investigative Study Consortium (BRISC) is a prospective longitudinal multisite study of 58 children diagnosed with BT. Premorbid functioning was assessed via retrospective parent report on standardized rating scales and detailed questionnaires. Findings were examined for the sample as a whole and in patients grouped by tumor histology (embryonal and non-embryonal). RESULTS: Mean age at diagnosis was 9.84 years (range, 3-16). The overall sample showed low proportions of pre/postnatal risk factors and delays in development. The proportion of children with clinically significant premorbid attention (18%) problems based on the BASC-2 exceeded expectation of that in healthy children (6.68%). Similar findings were obtained for somatization (18%) and anxiety (14%). Delays in talking were significantly more common in children with embryonal than non-embryonal tumors (P = 0.02). The non-embryonal tumor group had significantly higher overall rates of premorbid psychosocial problems than the embryonal tumor group (P < 0.001). CONCLUSIONS: We describe a rigorous approach to estimating premorbid developmental status in pediatric BT. The findings suggest mixed support for the "normalcy assumption" and highlight the complexity of this concept and need for further investigation. Our results also suggest the need for further study of potential premorbid correlates with tumor histology.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".