Propranolol therapy for infantile hemangioma is less toxic but longer in duration than corticosteroid therapy
Bibliographic record
Abstract
T he vast majority of vascular anomalies of infancy and childhood can be classified as hemangioma or vascular malformation (1).Infantile hemangiomas (IHs), characterized by proliferating endothelial cells, are the most common benign vascular malformations observed in children.They often present a few weeks after birth with a rapid proliferating phase, followed by a period of quiescence and, finally, involution (2).Given this characteristic natural history, the majority of hemangiomas do not require treatment.Pharmacological therapy with corticosteroids or interferon-alpha-2a is indicated for lesions that threaten vital function or are grossly deforming (1).More recently, two new developments have had a significant impact on the management of IHs, namely the development of multidisciplinary clinics and the introduction of the beta-blocker propranolol as a therapeutic option (2).A multidisciplinary clinic provides many advantages, including effective communication among care providers, more accurate and protocolized gathering of information from patients, and allows for more treatment options and coordination of surgical and nonsurgical approaches (3).In our centre, a nurse, a professional photographer, two pediatricians and a plastic surgeon comprise such a multidisciplinary vascular malformation clinic.The traditional oneon-one approach is maintained when the patient is assessed.The team proposes the therapeutic options, if necessary.The clinic was established when propranolol was introduced as a novel therapeutic option and we were interested in the effect of the new therapy in a multidisciplinary setting.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".