QOL-48. SEIZURE IN PEDIATRIC PATIENTS WITH BRAIN TUMORS
Bibliographic record
Abstract
Seizures are one of the most common symptoms of pediatric brain tumors. The purpose of this study was to determine seizures frequency and associated risk in pediatric patients with brain tumors. A retrospective study was conducted in a single center over a period of 13 years. Data including demographic, tumor location, pathology, extend of resection, seizure characteristics were collected. A logistic regression model was built to determine which predictors are associated with the occurrence of seizures. Among the 329 children with primary brain tumors, 62 (18.8 %) experienced seizures. Children with cortical tumors were higher at risk to present seizures than patients with infratentorial tumors (OR=96.2; 95% CI=28.8-319.2; p<0,0001). All patients with dysembryoplastic neuroepithelial tumor (DNET, 7/7) and 80% of glioneuronal tumors (8/10) experienced seizures. Twenty-nine patients had focal seizures with impaired awareness (46.8 %), while 25 had focal to bilateral tonic-clonic seizures (40.3 %), and 8 had focal aware seizures (12.9 %). Twenty-nine patients (46.8 %) were eventually seizure-free after antiepileptic drugs withdrawal, while ten patients (16.1 %) had refractory epilepsy. Our study is one of the largest cohorts of children with tumor-related seizures and brings new insight in term of seizures frequency, risk factors and evolution following treatment.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".