MB-68HEALTH-RELATED QUALITY OF LIFE IN MOLECULAR SUBGROUPS OF MEDULLOBLASTOMA
Bibliographic record
Abstract
BACKGROUND: Medulloblastoma comprises at least four molecular subgroups (WNT, SHH, Group 3, and Group 4) with distinct demographic, genetic and clinical features. Treatment for medulloblastoma is associated with long-term physical, endocrine and neuropsychological impairments that may negatively influence health-related quality of life (HRQL). The goal of the present study was to evaluate HRQL in medulloblastoma subgroups. METHODS: The Health Utilities Index, Mark 2 (HUI-2), was collected from 72 patients (7 WNT, 18 SHH, 14 Group 3, 33 Group 4) across 10 sites in the Medulloblastoma Advanced Genomic International Consortium (MAGIC). The HUI-2 questionnaire is designed to provide utility scores for overall HRQL, and for single-attributes (i.e., sensation, mobility, cognition, self-care, emotion and pain). RESULTS: Although subgroups did not differ in overall HRQL, cognition differed between the subgroups (P = 0.04); the mean utility score was higher for SHH (0.98 + 0.01) than Group 4 (0.96 + 0.01), P = 0.047. This difference did not survive when controlling for a number of medical and demographic variables. Instead, mobility and self-care differed between the subgroups (all P < 0.05): the mobility utility score was higher for SHH (1.0 + 0.01) than WNT (0.95 + 0.01), P = 0.01; the self-care utility score was higher for both SHH (1.0 + 0.01) and Group 4 (0.99 + 0.01) than WNT (0.93 + 0.02), all P < 0.05. CONCLUSION: SHH patients appear to be most resilient across the cognition, mobility and self-care attributes, whereas WNT patients seem most vulnerable in the latter two. These findings improve our understanding of long-term outcome in medulloblastoma subgroups.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".