Bibliographic record
Abstract
PURPOSE OF REVIEW: The advent of integrated genomics revealed profound insights into medulloblastoma pathogenesis. However, these biological findings have yet to be translated into the clinic, as current treatment comprises surgical resection, conventional irradiation, and chemotherapy resulting in significant long-term sequelae. We sought to highlight the potential areas for targeted therapy based on our new understanding of the subgroup-specific tumor biology. RECENT FINDINGS: Recently, four distinct molecular subgroups of medulloblastoma have been identified [WNT (wingless), SHH (sonic hedgehog), Group 3, and Group 4]. Profiling of these subgroups revealed distinct genomic events, several of which represent actionable targets for therapy. Specifically, stratification of patients into their respective subgroups has profound prognostic impact, wherein therapy can be de-escalated in patients with favorable prognosis, and intensified therapy or novel agents can be considered in patients with poor prognosis. Novel subgroup-specific therapies are being explored in clinical trials, particularly for the SHH subgroup. Epigenetic modifiers are also recurrently affected in medulloblastoma suggesting that epigenetic therapy can be considered in a subset of patients. SUMMARY: The identification of subgroup-specific, actionable therapeutic targets has the potential to revolutionize therapy for medulloblastoma patients, and result in significantly improved quality of life in survivors and improved overall survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".