The diverse manifestations of tuberous sclerosis complex: the experience of a provincial TSC clinic
Bibliographic record
Abstract
Background: Recent consensus recommendations for Tuberous Sclerosis Complex (TSC) stress the importance of multidisciplinary follow-up for these patients. The objective of our study was to review the manifestations of TSC seen in our hospital to determine the care needs of this population. Methods: This was a systematic, retrospective chart review of children with TSC treated at our institution. Patients were identified through epilepsy and clinical neurophysiology databases. Results: The study population comprised 81 patients, born between 1987-2014, who were a median 10 (Range 0.2-23.2) years of age at last follow-up. 88% of patients had epilepsy, including 30% with a history of infantile spasms. Developmental delay was reported in 65%, while 40% had intellectual disability. Psychiatric co-morbidities occurred in 49%. The most common psychiatric diagnoses were autism (25%), ADHD (19%), and anxiety (16%). Cardiac rhabdomyomas occurred in 35% of patients and renal angiomyolipomas in 42%, while only 4% had polycystic kidneys. Subependymal giant cell astrocytomas were observed in 14% of patients. 86% had skin manifestations. Conclusions: This study reaffirms the multi-system manifestations of TSC and the need to provide comprehensive, multidisciplinary care. As many children are still very young, the prevalence of autism and intellectual disability is likely underestimated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".