Sirolimus Leads to Rapid Improvement in Fibroadipose Vascular Anomalies
Bibliographic record
Abstract
Purpose: Fibroadipose vascular anomaly (FAVA) is a complex vascular anomaly associated with postzygotic somatic PIK3CA mutations. FAVAs can cause significant pain, swelling, and musculoskeletal dysfunction. Treatment options are limited. Sirolimus is a well-tolerated and effective treatment for patients with FAVA. We report our experience of using sirolimus to treat 11 children with FAVAs. Methods: We conducted a retrospective review of all patients with FAVA treated with sirolimus in our institution. Results: Fourteen patients (10 females) were referred for sirolimus therapy for FAVA. Eleven patients were initiated on sirolimus at a mean age of 14 years (range: 9–17.9 years) and were then treated for a mean of 19 months (range: 1–46 months). Five had previously undergone sclerotherapy without benefit. Sirolimus was initiated at a dose of either 2.5 mg/m2 once daily or 0.8 mg/m2 twice daily. Doses were titrated to maintain sirolimus trough levels of 5–15 ng/L. Goals of treatment were improvement in pain and musculoskeletal dysfunction. All 11 patients reported reduced pain; 7 reporting this within 3 weeks of starting sirolimus. This allowed for discontinuation of analgesia. Function improved significantly in 9 of 11, leading to resumption of sports or work participation. Sirolimus side effects were similar to prior reports, most commonly mouth sores, mildly elevated lipids and acne. There was no grade III/IV toxicity. Conclusion: Sirolimus is a well-tolerated and effective treatment for patients with FAVA. Initial symptom improvement is rapid, with significantly reduced pain and improved function. We believe that sirolimus should be considered for all patients with FAVA as a first-line therapy before surgical/interventional approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".