Histologic Correlates of Molecular Group 4 Pediatric Medulloblastoma: A Retrospective Canadian Review
Bibliographic record
Abstract
INTRODUCTION: The World Health Organization currently classifies medulloblastoma (MB) into four molecular groups (WNT, SHH, Group 3 and Group 4) and four histologic subtypes (classic, desmoplastic nodular, MB with extensive nodularity, and large cell/anaplastic). "Classic" MB is the most frequent histology, but unfortunately it does not predict molecular group or patient outcome. While MB may exhibit additional histologic features outside of the traditional WHO subtypes, the clinical significance of such features, in a molecular context, is unclear. METHODS: The clinicopathologic features of 120 pediatric MB were reviewed in the context of NanoString molecular grouping. Each case was evaluated for five ancillary histologic features, including: nodularity without desmoplasia (i.e., "biphasic", B-MB), rhythmic palisades, and focal anaplasia. Molecular and histological features were statistically correlated to clinical outcome using Chi-square, log-rank, and multivariate Cox regression analysis. RESULTS: While B-MB (N = 32) and rhythmic palisades (N = 12) were enriched amongst non-WNT/SHH MB (especially Group 4), they were not statistically associated with outcome. In contrast, focal anaplasia (N = 12) was not associated with any molecular group, but did predict unfavorable outcome. CONCLUSION: These data nominate B-MB as a surrogate marker of Groups 3 and particularly 4 MB, which may earmark a clinically significant subset of cases.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".