The state of pediatric tuberous sclerosis complex epilepsy care: Results from a national survey
Bibliographic record
Abstract
OBJECTIVE: Epilepsy associated with tuberous sclerosis complex (TSC) can be challenging to treat and is associated with significant disease burden. Our objective was to better understand the state of epilepsy care of TSC amongst pediatric neurologists in Canada, identify gaps in care and determine whether access to a dedicated TSC clinic has an impact on epilepsy management. METHODS: A survey was developed after a literature review and discussion amongst two pediatric epileptologists and one nurse practitioner with expertise in TSC about the state of epilepsy care of TSC patients in Canada. Canadian pediatric neurologists were asked to participate in sharing their experiences via an anonymous web-based survey through the Canadian League Against Epilepsy (CLAE) and the Canadian Neurological Sciences Federation (CNSF). RESULTS: Fifty-seven responses were received. Access to a dedicated TSC clinic was reported by 25% (n = 14). Sixty percent (n = 34) reported performing serial EEG monitoring in infants with TSC and 57% (n = 33) started prophylactic antiseizure therapy when EEG abnormalities were detected, regardless of whether there was access to a TSC clinic (P = .06 and P = .29, respectively). While 52% (n = 29) did not feel comfortable prescribing mTORi for epilepsy, 65% (n = 36) indicated they would consider it with additional training. Epilepsy surgery was offered in 93% (n = 13) of centers with a dedicated TSC clinic but only 45% of centers without a TSC clinic (n = 19) (P = .002). SIGNIFICANCE: Our findings demonstrate the variability in neurological care of pediatric patients with TSC as it pertains to epilepsy management. There is a need for the establishment of epilepsy practice guidelines and a national network to support clinical practice, research, and education.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".