NCMP-12. GLIOMA RELATED EPILEPSY: CLINICAL AND PATHOLOGICAL CORRELATES
Bibliographic record
Abstract
The overall incidence of brain tumor related epilepsy among all causes of epilepsies ranges from 4-8%. Epileptic seizures are associated with brain tumor in approximately 30-50% of patients. Glioma is the most common cause of primary brain tumors, contributing nearly 50% of all cases. In high grade glioma seizure frequency is 20-50%, but in low grade it can be up to 90%. There have been multiple publications describing epilepsy frequency in low grade and high-grade glioma but few have described the relationship between seizures and IDH1 R132H mutation. This is an observational retrospective clinical study. Medical records and neuroimaging data were reviewed for all newly diagnosed patients with brain tumor and pathology consisting of oligodendroglioma and astrocytoma WHO grade II and III at the Montreal Neurological Institute and Hospital between 2011 and 2015. IDH1 R132H status was obtained in all tumors and 1p/19q co-deletion was analyzed in all oligodendrogliomas. The details about related seizures were collected, including seizure semiology, timing in relation to surgery, frequency, and antiepileptic medications used. Tumor location was determined by preoperative MRI. There were a total of 103 subjects included in the study. Preoperative seizure frequency was 66% (n=68). There was no association between single or multi-lobe location of tumor and preoperative seizure frequency (n=103, p=0.186). Tumors with mutated IDH1 had a higher rate of preoperative seizure at presentation (74%, n=62 vs 49%, n=37, p=0.007). Seizure freedom at 1 year was increased by gross total resection compared to subtotal resection or biopsy (90%, n= 20 vs 67%, n=55, p=0.049). Presence of an IDH1 mutation, but not tumor location, is associated with higher risk of pre-operative seizure in low and intermediate grade glioma. Extent of surgical resection may influence seizure control at 1 year in patients with low and intermediate grade glioma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".