Molecular genetics of supratentorial primitive neuroectodermal tumors and pineoblastoma
Bibliographic record
Abstract
The supratentorial primitive neuroectodermal tumors (PNETs) are a group of highly malignant lesions primarily affecting young children. Although these tumors are histologically indistinguishable from infratentorial medulloblastoma, they often respond poorly to medulloblastoma-specific therapy. Indeed, existing molecular genetic studies indicate that supratentorial PNETs have transcriptional and cytogenetic profiles that are different from those of medulloblastomas, thus pointing to unique biological derivation for the supratentorial PNET. Due to the rarity of these tumors and disagreement about their histopathological diagnoses, very little is known about the molecular characteristics of the supratentorial PNET. Clearly, future concerted efforts to characterize the molecular features of these rare tumors will be necessary for development of more effective supratentorial PNET treatment protocols and appropriate disease models. In this article the authors review existing molecular genetic data derived from human and mouse studies, with the aim of providing some insight into the putative histogenesis of these rare tumors and the underlying transforming pathways that drive their development. Studies of the related but distinct pineoblastoma PNET are also reviewed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".