Case report: Pediatric low-grade gliomas: a fine balance between treatment options, timing of therapy, symptom management and quality of life
Bibliographic record
Abstract
Introduction: Pediatric low-grade gliomas (pLGG) are the most common brain tumor in children and encompass a wide range of histologies. Treatment may pose challenges, especially in those incompletely resected or those with multiple recurrence or progression. Case description: We report the clinical course of a girl diagnosed with pilocytic astrocytoma and profound hydrocephalus at age 12 years treated with subtotal resection, vinblastine chemotherapy, and focal proton radiotherapy. After radiotherapy the tumor increased in enhancement temporarily with subsequent resolution consistent with pseudoprogression. Despite improvement in imaging and radiographic local control, the patient continues to have challenges with headaches, visual and auditory concerns, stroke-like symptoms, and poor quality of life. Conclusion: pLGG have excellent long-term survival; thus, treatments should focus on maintaining disease control and limiting long-term toxicities. Various treatment options exist including surgery, chemotherapy, targeted agents, and radiation therapy. Given the morbidity associated with pLGG, individualized treatment approaches are necessary, with a multi-disciplinary approach to care focused on minimizing treatment side effects, and promoting optimal quality of life for patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".