Predictive Factors for Epilepsy in Pediatric Patients with Sturge Weber Syndrome (P3.253)
Bibliographic record
Abstract
Objective:The aim of this study is to identify potential predictive factors predisposing Sturge Weber patients to epilepsy. Background:Sturge Weber Syndrome (SWS) is a rare neurocutaneous condition characterized by a facial angioma associated with a retinal angioma and/or a cerebral pial angioma. The former can lead to glaucoma and the latter to several neurological problems including refractory epilepsies. Since the pial angioma develops after birth, patients at risk of epilepsy are often not identified until their first seizure. Methods:The medical photography database of our institution has been retrospectively reviewed to identify all patients with a diagnosis of SWS followed between 1993 and 2013. SWS patients with isolated glaucoma were compared to patients with epilepsy regarding the laterality (bilateral vs unilateral) of the facial angioma, its location, the presence of asymmetrical background activity on electroencephalograms (EEG) done prior epilepsy onset and cerebral imaging characteristics. Logistical regression tests and a p value of 0.05 were used. Results:To date, 21 patients with SWS have been identified. 12 of them had isolated glaucoma. No significant difference was noted when patients were compared based on the laterality of the lesion (p=0.169), or the location of the facial angioma (p = 0.314 to 0.999). Only 2 epileptic patients had digital EEG done prior the onset of epilepsy and only 2 patients with glaucoma had digital EEG done during their follow up. No significant difference was noted between EEG background activities in the two groups (p= 0.514). Among cerebral anomalies, the presence of venous drainage anomalies strongly predicted (p = 0.004) the onset of epilepsy. Conclusions:The presence of cerebral venous drainage anomalies greatly increases the risk of epilepsy in SWS patients. Since these anomalies can be detected at birth, they might guide the management regarding follow up and preventive treatment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".