Adolescent and young adult glioma: systematic review of demographic, disease, and treatment influences on survival
Bibliographic record
Abstract
Background: Prognostic factors in adolescent and young adult (AYA) glioma are not well understood. Though clinical and molecular differences between pediatric and adult glioma have been characterized, their application to AYA populations is less clear. There is a major need to develop more robust evidence-based practices for managing AYA glioma patients. Methods: A systematic review using PRISMA methodology was conducted using multiple databases with the objective of identifying demographic, clinical, molecular and treatment factors influencing AYA glioma outcomes. Results: 40 Studies met inclusion criteria. Overall survival was highly variable across studies depending on glioma grade, anatomic compartment and cohort characteristics. Thirty-five studies suffered from high risk of bias in at least one domain. Several studies included older adults within their cohorts; few captured purely AYA groups. Despite study heterogeneity, identified favorable prognosticators included younger age, higher functional status at diagnosis, low-grade pathology, oligodendroglioma histology and increased extent of surgical resection. Though isocitrate dehydrogenase (IDH) mutant status was associated with favorable prognosis, validity of this finding within AYA was compromised though may studies including older adults. The prognostic influence of chemotherapy and radiotherapy on overall survival varied across studies with conflicting evidence. Conclusion: Existing literature is heterogenous, at high risk of bias, and rarely focused solely on AYA patients. Many included studies did not reflect updated pathological and molecular AYA glioma classification. The optimal role of chemotherapy, radiotherapy, and targeted agents cannot be determined from existing literature and should be the focus of future studies.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".