Molecular and clinical correlates of medulloblastoma subgroups
Bibliographic record
Abstract
Medulloblastoma is a major cause of cancer-related morbidity and mortality in children, as a significant proportion of patients succumb to their disease and most survivors are left with life-long sequelae of therapy. Prior medulloblastoma classification systems relied heavily on histology and failed to account for tumor biology. The upcoming 2021 WHO classification of central nervous system tumors now firmly establishes that medulloblastoma actually comprises at least four distinct molecular entities, with considerable substructure within each group. For the first time, the study design of contemporary clinical trials has now recognized the molecular heterogeneity of medulloblastoma. The incorporation of routine molecular subgrouping into upcoming clinical trials has the potential to significantly improve survival and quality of life for children and adults diagnosed with medulloblastoma. This review was conducted to summarize these recent advances in the genomics of medulloblastoma and to summarize the timely results of molecularly-informed published clinical trials. Specifically, English language literature will be reviewed in addition to the results of SJMB03, ACNS0331, and ACNS0332.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".