A single center experience in the management of progressive juvenile pilocytic astrocytoma
Bibliographic record
Abstract
BACKGROUND: Juvenile pilocytic astrocytoma (JPA) typically follows an indolent clinical course. The first-line treatment for most JPAs is surgical resection. However, a gross total resection may not be feasible for deep-seated lesions and/or infiltrative tumors, leading to multimodal treatment approaches that may be complicated by patient age and tumor location. Despite the prevalence of pediatric JPAs, there is no single approach to treating progressive disease. METHODS: We investigated the multifaceted management of progressive JPAs through a retrospective analysis of JPAs treated at a single center over an 18-year period (1998-2016). All cases were categorized according to location, whether supratentorial or infratentorial, and for each case we calculated the number of interventions and the time between interventions. RESULTS: We identified a total of 40 JPAs, (11 supratentorial, 29 infratentorial). Total number of interventions among all supratentorial JPA patients was 21 (average 2 interventions/patient). The total number of interventions among infratentorial JPAs was 40 (average 1.4 interventions/patient). CONCLUSIONS: Treatment of progressive JPA is variable and may require numerous surgeries and adjuvant therapies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".