Bibliographic record
Abstract
Abstract Medulloblastoma (MB), the most common malignant paediatric brain tumour, arises in the cerebellum. Recent high‐throughput technology that includes ribonucleic acid‐based expression analysis and deoxyribonucleic acid (DNA)‐based copy number analysis, as well as whole‐genome sequencing studies have unravelled the mysteries of this embryonic tumour from clinical, histological and molecular standpoints. Studies of hereditary syndromes associated with MB have led to an increased understanding of the genomics of this tumour. Signalling pathways and growth factors, which are crucial in normal cerebellum development, are also involved in MB formation. The molecular discoveries in the last decade have led to advances in two major areas: (1) The clinical and molecular subgrouping of patients and their stratification; and (2) research being performed to individualise treatments according to the genetics of the patient's tumour. The use of extensive cancer genetic databases will ensure that future discoveries in MB pathogenesis will be made. Key Concepts: There is more than one cell of origin for MB. MBs are genetically and histologically heterogeneous. MB growth is similar to the normal growth of the cerebellum but it is growth gone awry. Signalling pathways play significant roles in cerebellar growth. Mutations in these pathways lead to tumour formation. Growth factor and DNA repair pathway mutations may drive MB formation. MB in adults is different from that in children. Metastatic MB has specific genomics that are different from the primary tumour in the same patient. The new molecular discoveries have important prognostication impact. The next era is to individualise novel therapies against each patient's MB.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".