Tumors of the Central Nervous System: Clinical Aspects, Molecular Mechanisms, Unanswered Questions, and Future Research Directions
Bibliographic record
Abstract
Central nervous system tumors are the most common solid tumors in children. Many histological subtypes and biological variants exist. The 2007 Neurobiology of Disease in Children Symposium, held in conjunction with the 36th annual meeting of the Child Neurology Society, aimed to define current knowledge in the field and to develop specific aims for future clinical, translational, and fundamental science. Because of advances in structural and metabolic imaging, surgical technique, and combination therapies, the life expectancy of children with some of the most common tumors, such as cerebellar astrocytomas and medulloblastomas, has improved. Other common tumor types, including diffuse pontine gliomas and malignant embryonal tumors, still have a dismal prognosis. As novel therapies are identified for pediatric central nervous system tumors, long-term survival may be associated with considerable disability. A cooperative effort is crucial to early diagnosis and to translating basic research findings into safe, effective new treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| 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".