Has Propranolol Eradicated the Need for Surgery in the Management of Infantile Hemangioma?
Bibliographic record
Abstract
BACKGROUND: The authors assessed the impact of propranolol as the first-line treatment of infantile hemangioma (IH) on the need for surgery in the management of IH. METHODS: In this retrospective study, 420 patients with IH referred to a multidisciplinary center between January of 2005 and August of 2014 were included. Clinical data, including sex; age at first consultation and at treatment initiation; location, size, number, aspect, and complications of IH; and type of treatment were collected. Statistical analyses were conducted considering each patient and each tumor independently. RESULTS: A total of 625 IHs (420 patients) were reviewed; 113 patients had more than 1 IH (26.91%). Median age at first consultation was 7 months. Overall, 243 patients were treated (57.86%) using surgery ( n = 128 patients, 141 IHs), propranolol ( n = 79 patients, 89 IHs), corticosteroids ( n = 51 patients, 56 IHs), or laser ( n = 34 patients, 36 IHs). Propranolol was effective in all but 2 infants with IH. Seven of 79 patients (8.86%) initially treated with propranolol still required surgery, in contrast to 18 of 51 patients (35.29%) initially treated with corticosteroids and 103 of 290 patients (35.51%) with no medical treatment. Since the availability of propranolol, patients were less likely to undergo surgery (48 versus 80 patients; P < 0.001). This demonstrated that the use of propranolol reduced the need for surgery ( P < 0.001; OR, 0.177; 95% CI, 0.079 to 0.396). CONCLUSIONS: Propranolol dramatically reduced the need for surgery, regarding indications and number of patients. Surgical correction remains important for sequelae management, nonresponders, or strawberry-like IH. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".