Propranolol in the Treatment of Problematic Infantile Hemangioma: Review of 35 Consecutive Patients from a Vascular Anomalies Clinic
Bibliographic record
Abstract
BACKGROUND: Propranolol, a nonselective β-blocker, has been reported as efficient for controlling the growth of complicated infantile hemangiomas (IHs). No uniformly accepted protocol exists regarding the administration of oral propranolol for IH. OBJECTIVE: We sought to share our experience using propranolol for problematic IH and to evaluate the efficacy of this treatment modality. METHODS: A retrospective chart review analysis was performed for 35 consecutive children treated with propranolol as an oral solution on an outpatient basis in our dermatology/vascular anomalies clinic. A protocol was established with the help of our pediatric cardiologists, including pretreatment electrocardiography and echocardiography. Medical photographs taken after 2 months of treatment were rated by two independent evaluators. RESULTS: We treated 31 girls and 4 boys with a median age of 3.5 months. Rapid improvement was reported in the first days of treatment in 34 patients. Mean improvement after 2 months was 61.5%. No serious adverse effects were reported. CONCLUSION: Propranolol was effective in controlling the proliferative phase of problematic IH. It was well tolerated in our study. Outpatient treatment is possible if parents follow strict guidelines. Propranolol should be a first-line treatment for problematic IH in carefully selected patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".