An update on multimodal management of craniopharyngioma in children
Bibliographic record
Abstract
Craniopharyngioma (CP) represent 1.2-4.6% of all intracranial tumors in children and carry a significant morbidity due to their lesional intimacy with structures involved in neurological, visual, and endocrinological functions. Variable treatment modalities being available, including surgery, radiation therapy, alternative surgeries, and intracystic therapies or combinations of them, their common goal is to reduce immediate and long-term morbidity while preserving these functions. Multiple attempts have been made to re-evaluate surgical and irradiation strategies in order to optimize their complication and morbidity profile. However, despite significant advances in "function sparing" approaches, such as limited surgery and improved technologies of radiation therapies, achieving interdisciplinary consensus on the optimal treatment algorithm remains a challenge. Furthermore, there remains a significant span of improvement given the number of specialties involved as well as the complex and chronic nature of CP disease. This perspective article aims to summarize recent changes and knowledge gains in the field of pediatric CP, outlining updated treatment recommendations, a concept of integrative interdisciplinary care and the implication of novel potential diagnostic tools. A comprehensive update on the multimodal treatment of pediatric CP is presented, focusing on "function-preserving" therapies and their implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".