Evaluating the epilepsy and oncological outcomes of pediatric brain tumors causing seizures.
Bibliographic record
Abstract
We read with great interest a single center retrospective cohort study by Wessling et al. titled “Brain tumors in children with refractory seizures—a long-term followup study after epilepsy surgery”. This is a well-written and concise study of the post-surgical oncological, epileptological, and psychosocial follow-up of children with seizures secondary to brain tumors. This single-center cohort study includes 107 patients (aged 1.6–17.7 years), treated between 1988 and 2012, the majority with lowgrade tumors (76.6% WHO I, 19.6% WHO II). Tumor localization was temporal in more than half of the patients, and histopathology included gangliogliomas in 57% of the cases, gliomas in 29%, and dysembryoplastic neuroepithelial tumors (DNET) in 14%. In comparison with other studies of post-surgical seizure outcomes in literature, this cohort consisted of disproportionally more ganglioglioma cases—traditionally considered benign tumors with low rates of oncological recurrence and favorable post-surgical seizure outcomes. Post-operative complications, including a 5.6% chance of permanent new deficits and a 2.8% chance of surgical complications, were comparable to other studies in literature.
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 | 0.025 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".