Updated clinical recommendations for the management of tuberous sclerosis complex associated epilepsy
Bibliographic record
Abstract
Children with tuberous sclerosis complex (TSC), may experience a variety of seizure types in the first year of life, most often focal seizure sand epileptic spasms. Drug resistance is seen early in many patients, and the management of TSC associated epilepsy remain a major challenge for clinicians. In 2018 clinical recommendations for the management of TSC associated epilepsy were published by a panel of European experts. In the last five years considerable progress has been made in understanding the neurobiology of epileptogenesis and three interventional randomized controlled trials have changed the therapeutic approach for the management of TSC associated epilepsy. Pre-symptomatic treatment with vigabatrin may delay seizure onset, may reduce seizure severity and reduce the risk of epileptic encephalopathy. The efficacy of mTOR inhibition with adjunctive everolimus was documented in patients with TSC associated refractory seizures and cannabidiol could be another therapeutic option. Epilepsy surgery has significantly improved seizure outcome in selected patients and should be considered early in all patients with drug resistant epilepsy. There is a need to identify patients who may have a higher risk of developing epilepsy and autism spectrum disorder (ASD). In the recent years significant progress has been made owing to the early identification of risk factors for the development of drug-resistant epilepsy. Better understanding of the mechanism underlying epileptogenesis may improve the management for TSC-related epilepsy. Developmental neurobiology and neuropathology give opportunities for the implementation of concepts related to clinical findings, and an early genetic diagnosis and use of EEG and MRI biomarkers may improve the development of pre-symptomatic and disease-modifying strategies.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".