Determinants of quality of life outcomes for survivors of pediatric brain tumors
Bibliographic record
Abstract
INTRODUCTION: To describe the quality of life (QOL) of pediatric brain tumor survivors (PBTSs) prospectively and to identify potential medical, personal and family contextual factors associated with QOL. METHODS: Ninety-one PBTSs (8-16 years) who were off treatment and attending a regular classroom participated. Self- and caregiver-proxy-reported on QOL at baseline, 2 and 8 months. At baseline, cognitive, executive function, attention and memory, medical and demographics information were attained. RESULTS: Significant improvements over time in PBTS's emotional QOL were self- and proxy-reported (P < 0.01) and global QOL proxy-reported (P = 0.04). Receiving cranial irradiation therapy (CIT) and poor behavioral regulation predicted poor global QOL scores reported by both informants (P < 0.017). Poor behavioral regulation also predicted poor self-reported school functioning, and poor proxy-reported emotional and social QOL (P < 0.037). Boys reported better emotional QOL (P = 0.029), and PBTSs over 11 years old were reported to have better emotion and school-related QOL. Finally, being non-White and having low income predicted poor self-reported global and emotional QOL (P = 0.041). CONCLUSIONS: Receiving CIT, having poor behavioral regulation, being a female, under 11 years old and coming from low-income, non-White families place PBTSs at risk for poor QOL.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".