Tumors of the skull base in children: review of tumor types and management strategies
Bibliographic record
Abstract
Although many treatment strategies for skull base tumors in adults have been reported, relatively little has been reported regarding such therapies in the pediatric population. Skull base tumors in children present a therapeutic challenge because of their unique pathological composition, the constraints of the maturing skull and brain, and the small size of the patients. In this review, the authors examine the pediatric skull base lesions that occur in the anterior, middle, and posterior cranial base, focusing on unique pediatric tumors such as encepahalocele, fibrous dysplasia, esthesioneuroblastoma, craniopharyngioma, juvenile nasopharyngeal angiofibroma, cholesteatoma, chordoma, chondrosarcoma, and Ewing sarcoma. They review management strategies that include radio- and chemotherapy, as well as surgical approaches with emphasis on the modifications and complications associated with the procedures as they apply in children. Evidence for the advantages and limitations of radiotherapy, chemotherapy, and surgery as it pertains to the pediatric population will be examined. With a working knowledge of skull base anatomy and special considerations of the developing craniofacial skeleton, neurosurgeons can treat skull base lesions in children with acceptable morbidity and mortality rates. Outcomes in this population may be better than those in adults, in part because of the benign histopathology that frequently affects the pediatric skull base, as well as the plasticity of the maturing nervous system.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".