Germline predisposition to glial neoplasms in children and young adults
Bibliographic record
Abstract
Gliomas are the most common malignancies of the central nervous system (CNS). A significant proportion of both low- and high-grade gliomas in children, adolescents, and young adults have specific genetic events which can be traced to the germline. Despite integration of genomic findings in recent CNS tumor classifications, germline origins of these genetic events are seldom highlighted. These cancer predisposition syndromes can predispose the individual and family members to multiple cancers in different organs beyond the CNS and to other non-oncologic manifestations caused by the genetic dysfunction. Recent molecular discoveries and careful surveillance have resulted in improved survival and reduced morbidity for many of these conditions. Importantly, identifying a genetic predisposition can alter treatment of the existing malignancy, by mandating the use of a different protocol, targeted therapy, or other novel therapies. Hence, prompt diagnosis is sometimes crucial for these young patients. High index of suspicion and early referral to genetic testing and counseling are important and may be beneficial to these families. In this review, we discuss the clinical manifestations, genetics, tumor management, and surveillance in these patients. These provide insights into the complex mechanisms in glioma-genesis that can impact the treatment and survival for these patients and families in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".