Management of adult tuberous sclerosis complex-related angiomyolipoma: A single-center experience
Bibliographic record
Abstract
INTRODUCTION: Tuberous sclerosis complex (TSC) is a rare, multi-system, genetic disease. A significant cause of TSC-related morbidity is potential bleeding from renal angiomyolipoma (AML). To pre-emptively decrease AML bleeding, mTOR inhibitors can be used; however, thresholds for initiating and maintaining everolimus therapy remain uncertain. Recent literature suggests not triggering active treatment of AMLs based on size thresholds alone. We evaluated the appropriateness of initiating everolimus therapy in asymptomatic patients after considering AML size, rate of growth, and other factors. METHODS: Diagnostic criteria developed by the 2012 International TSC Consensus Group and presence of AML were used as inclusion criteria. Medical and imaging reports of 11/20 TSC patients from a single center were reviewed. RESULTS: Mean age was 40.55 (±16.27) and 11 patients were female. Eight asymptomatic patients at high risk for complications underwent everolimus therapy, of which seven (88%) demonstrated decreased AML size, but multiple side effects were reported. Four high-risk asymptomatic patients did not undergo therapy due to side effect concerns, while four low-risk asymptomatic patients had stable AMLs under active surveillance. Four patients had reduced AMLs through local therapy. CONCLUSIONS: Everolimus treatment was effective for managing AML size in most high-risk, asymptomatic patients with tolerable side effects. AML size can remain relatively stable for asymptomatic, low-risk patients despite not receiving intervention(s). Patients with TSC-related AML can be safely managed with mTOR inhibitors like everolimus with shared decision-making, including factors such as bleeding risk, AML growth rate, and number and absolute size of AMLs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".