MB-104GENETIC PREDICTORS OF INTELLECTUAL OUTCOME IN CHILDREN TREATED FOR MEDULLOBLASTOMA
Bibliographic record
Abstract
BACKGROUND: Advances in cancer treatment have led to improved survival rates for pediatric brain tumor patients. However, these treatments are toxic to healthy brain tissue, and many patients develop long-term neuropsychological deficits as a result1. There exists a large variability in outcome such that some patients develop functional deficits while others do not. Little is known about genetic factors that may predict better or worse outcome in these patients. We explored the relationship between two candidate genes, gluthathione S transferase (GST) and peroxisomal proliferator activated receptor (PPAR), and long-term intellectual outcome in a sample of medulloblastoma patients. METHODS: Germ-line DNA from blood samples of thirty-nine patients was sequenced using the Illumina Human Omni2.5 BeadChip. Patients received tumor resection, craniospinal radiation, and multiagent chemotherapy as part of treatment. Intellectual outcome was assessed via the full scale intelligent quotient composite (FSIQ) from the Wechsler Intelligence scale for Children. A machine learning algorithm2 was used to explore the relationship between genes and intellectual outcome. RESULTS: We identified five polymorphisms on the PPARδ gene that were significantly associated with changes in intellectual outcome. Variant alleles predicted declines in intellectual functioning while wild-type or heterozygous alleles predicted a net-zero change. Our findings are consistent with recent studies suggesting a putative role for PPARδ in the regulation of injury-related neurotoxicity. DISCUSSION: PPARδ accounts for the variability in outcome in medulloblastoma survivors. Our results lay the foundation for animal studies investigating the molecular mechanisms by which PPARδ confers protection against the effects of treatment for brain tumors.
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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 | 0.001 | 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 | 0.001 |
| 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".