Advances in managing medulloblastoma and intracranial primitive neuro-ectodermal tumors
Bibliographic record
Abstract
Medulloblastoma and central nervous system (CNS)-primitive neuro-ectodermal tumors (PNETs) are a diverse group of entities which encompasses different pathological and clinical pictures. Initially divided based on histology and location, molecular insight is leading to new definitions and a change in the borders delineating these diseases, such that they become more divergent. Current treatment approaches consist of surgical resection, radiotherapy and intensive chemotherapy, dependent on age. Stratification is one risk factor shown to be prognostic and is divided into high- and average-risks. Outcomes with modern treatment regimens are good, particularly in average-risk medulloblastoma patients, but the cost of cure is high, with high rates of neurocognitive, endocrine and social dysfunction. The changing biological landscape, however, may allow for clearer prediction of tumor behavior, to better identify "good" and "bad" players within these groups. Discovery of subgroups with changes in dependent molecular pathways will also lead to the development of new specific targeted therapies. Presenting exciting opportunities, these advances may transform the treatment for some patients, revolutionizing therapy in the future. Several challenges, however, are yet to be faced and caution is needed not to abandon previously defined prognostic factors on the strength of thus far retrospective evidence. We are witnessing a new era of trials with biological stratification involving multiple subgroups and treatment arms, based on specific tumor-related targets. This review discusses the changing face of medulloblastoma and CNS-PNETs and how we move molecular advances into clinical trials that benefit patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".