Rebound Growth of Infantile Hemangiomas After Propranolol Therapy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Propranolol is first-line therapy for problematic infantile hemangiomas (IHs). Rebound growth after propranolol discontinuation is noted in 19% to 25% of patients. Predictive factors for rebound are not completely understood and may alter the management approach. The goal of the study was to describe a cohort of patients with IHs treated with propranolol and to identify predictors for rebound growth. METHODS: A multicenter retrospective cohort study was conducted in patients with IHs treated with propranolol. Patient demographic characteristics, IH characteristics, and specifics of propranolol therapy were obtained. Episodes of rebound growth were recorded. Patients' responses to propranolol were evaluated through a visual analog scale. RESULTS: A total of 997 patients were enrolled. The incidence of rebound growth was 231 of 912 patients (25.3%). Mean age at initial rebound was 17.1 months. The odds of rebound among those who discontinued therapy at <9 months was 2.4 (odds ratio [OR]: 2.4; 95% confidence interval [CI]: 1.3 to 4.5; P = .004) compared with those who discontinued therapy between 12 to 15 months of life. Female gender, location on head and neck, segmental pattern, and deep or mixed skin involvement were associated with rebound on univariate analysis. With multivariate analysis, only deep IHs (OR: 3.3; 95% CI: 1.9 to 6.0; P < .001) and female gender (OR: 1.7; 95% CI: 1.1 to 2.6; P = .03) were associated. Of those with rebound growth, 83% required therapeutic modification including 62% of patients with modifications in their propranolol therapy. CONCLUSIONS: Rebound growth occurred in 25% of patients, requiring modification of systemic therapy in 15%. Predictive factors for rebound growth included age of discontinuation, deep IH component, and female gender. Patients with these predictive factors may require a prolonged course of therapy.
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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 | 0.000 |
| 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 | 0.001 |
| 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".