C.6 Efficacy and safety of mTOR inhibitor therapy in a Canadian paediatric tuberous sclerosis complex cohort
Bibliographic record
Abstract
Background: Tuberous Sclerosis Complex (TSC) is a genetic condition marked by multisystem benign tumours. mTOR inhibitor (mTORi) therapy is indicated for subependymal giant cell astrocytomas (SEGA), renal angiomyolipomas (AML), and drug-resistant epilepsy. This study aimed to evaluate the efficacy and safety of mTORi in our paediatric TSC cohort. Methods: Data on patient demographics, clinical outcomes, and adverse events (AEs) were obtained from SickKids’ prospective observational TSC Database (n=107). Results: 19 children (median age at diagnosis 0.6 years, range 0-8.3; F:M 10:9) received mTORi. Indications were SEGA (n=6), AML (n=4), seizures (n=4), prophylactic (n=2), AML/SEGA (n=1), seizures/AML (n=1), and seizures/SEGA (n=1). Median age at mTORi initiation was 8.4 years (range 2.1-15.4). 68.4% (n=13/19) received sirolimus and 31.6% (n=6/19) received everolimus. 24 months post-mTORi initiation, 50% showed stable SEGA (n=4/8), 50% reduced SEGA (n=4/8), 66.7% stable AML (n=8/12), 25% reduced AML (n=3/12), and 8.3% larger AML (n=1/12). Variability in reporting seizure frequency rendered mTORi effects on epilepsy inconclusive. mTORi was overall well tolerated, yet 100% (n=19/19) reported AEs, majority Grade 1-2. Conclusions: This study describes the efficacy and tolerability of mTORi in a Canadian paediatric TSC cohort, which demonstrates beneficial effects on SEGA and AML, with mild to moderate AEs reported.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".