D.02 Predictive factors for epilepsy in pediatric patients with Sturge Weber Syndrome
Bibliographic record
Abstract
Background: Sturge Weber Syndrome (SWS) patients at risk of epilepsy are often not identified before their first seizure which leads to unnecessary follow up of many patients with facial angioma. Methods: The medical photography database of our institution has been reviewed to identify SWS patients followed between 1993 and 2013. Patients with isolated glaucoma were compared to patients with epilepsy regarding the location of the facial angioma, the presence of asymmetrical background activity on EEG done prior epilepsy onset and cerebral imaging. Logistical regression tests and a p-value of 0.05 were used. Results: 21 patients with SWS have been identified. 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). The presence of venous drainage anomalies (VDA) predicted (p = 0.004) the onset of epilepsy. Conclusions: Cerebral VDA increases the risk of epilepsy in SWS patients. Since they can be detected at birth, they might guide the management.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".