Clinical and molecular features of disseminated pediatric low-grade glioma and glioneuronal tumors: a systematic review and survival analysis
Bibliographic record
Abstract
Background: Disseminated pediatric low-grade gliomas and glioneuronal tumors (dpLGG/GNTs) are associated with a poorer prognosis than nondisseminated pLGG/GNTs. To date there is no comprehensive report characterizing the genome profile of dpLGG/GNTs and their relative survival. This systematic review aims to identify the pattern of genetic alterations and long-term outcomes described for dpLGG/GNT. Methods: A systematic review of the literature was performed to identify relevant articles. A quality and risk of bias assessment of articles was done using the GRADE framework and ROBINS-I tool, respectively. Results: fusion (55%). BRAF p.V600E mutation was found in 7% of subjects. A higher proportion of subjects demonstrated primary dissemination compared to secondary dissemination (65% vs 25%). First-line chemotherapy consisted of an alkylation-based regimen and vinca alkaloids. Surgical intervention ranged from biopsy alone (59%) to surgical resection (41%) and CSF diversion (28%). Overall, 73% of cases were alive at last follow-up. Survival did not vary by tumor type or timing of dissemination. All studies reviewed either ranked low or moderate for both quality and risk of bias assessments. Conclusions: fusion were the most common alterations identified in dpLGG/GNT cases reviewed. The relative molecular heterogeneity between DLGG and DLGNT, however, deserves further exploration and ultimately correlation with their biologic behavior to better understand the pathogenesis of dpLGG/GNT.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".