P.087 The landscape of paediatric tuberous sclerosis complex (TSC) neurological care in canada: results from a national survey
Bibliographic record
Abstract
Background: Tuberous Sclerosis Complex (TSC) is a genetic disease that affects multiple body systems with the neurological manifestations causing the greatest disease burden. The objective of this study was to understand the scope of neurological TSC care delivery across Canada. Methods: A survey was developed after literature review and discussion amongst two Paediatric Epileptologists and one Nurse Practitioner with expertise in TSC. Canadian Paediatric Neurologists participated via an anonymous web-based survey through the Canadian League Against Epilepsy (CLAE) and Canadian Neurological Sciences Federation (CNSF). Results: Fifty-eight responses were received. A dedicated TSC clinic was reported by 24% (n=14). Sixty percent (n= 35) reported preforming serial screening EEG monitoring in infants and 58% (n= 34) started prophylactic therapy when EEG abnormalities occurred. Vigabatrin was used in 37% (n=21). For management of drug-resistant epilepsy, surgery was reported as the preferred therapeutic option in 57% (n=32) of respondents. Barriers to treatment identified were a lack of multi-disciplinary care, unfamiliarity with new therapies and insufficient resources. Conclusions: Our findings demonstrate the variability in neurological care delivery of patients with TSC. With few dedicated TSC clinics, there is a need for the establishment of 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".