Molecular Insight into Medulloblastoma and Central Nervous System Primitive Neuroectodermal Tumor Biology from Hereditary Syndromes: A Review
Bibliographic record
Abstract
Through the study of uncommon familial syndromes, physicians and scientists have been able to illuminate the underlying mechanisms of some of the more common sporadic diseases; this is illustrated best by studies of familial retinoblastoma. A number of rare familial syndromes have been described in which affected individuals are at increased risk of developing medulloblastoma and/or supratentorial primitive neuroectodermal tumors. The descriptions of many of these syndromes are based on patients observed by clinicians in their clinical practice. Determination of the underlying genetic defects in these patients with uncommon syndromes has led to identification of a number of genes subsequently found to be mutated in sporadic medulloblastomas (tumor suppressor genes). Associated genes in the same signaling pathways have also been found to be abnormal in sporadic medulloblastoma. Identification of patients with these rare syndromes is important, as they are often at increased risk for additional neoplasms, as are family members and future children. We review the published literature describing hereditary syndromes that have been associated with an increased incidence of medulloblastoma and/or central nervous system primitive neuroectodermal tumor. Review of the underlying molecular abnormalities in comparison to changes found in sporadic neoplasms suggests pathways important for tumorigenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".