55 Evidence behind drug use in vascular anomalies: From infantile hemangioma to rare vascular anomalies … What do we know about what we do with local, systemic, or sclerotherapy treatment?
Bibliographic record
Abstract
Abstract Primary Subject area Clinical Pharmacology and Toxicology Background The most common vascular anomaly (VA) requiring medical treatment are infantile hemangiomas, but many other vascular anomalies affecting children are treated with local, systemic drugs or sclerosing agents. Rational drug prescribing implies assessment of whether a drug’s benefits outweigh the risk of adverse effects for treatment of a specific vascular anomaly. This process relies on the quality of the literature on efficacy and safety for drug treatment of vascular anomalies. Objectives To evaluate the level of evidence surrounding drug use in vascular anomalies. Design/Methods A list of drugs used in vascular anomalies was created with existing guidelines. For each drug, the article displaying the highest level of evidence was determined, using Oxford criteria. Levels of evidence were compared between efficacy and safety data, routes of administration, pharmacological categories, and a subset of specific vascular anomalies. The influence of research quality on study results was explored by comparing the percentage of clinical efficacy between high- and low-quality studies. Results We identified 71 different drugs for treating vascular anomalies. The median level of evidence was low, with a predominance of retrospective cohort studies and case reports. The level of evidence was higher for efficacy than safety data and for common diseases like infantile hemangiomas. The level of evidence was lower for systemic vs. local drugs. Clinical efficacy was more frequently reported in low quality studies (retrospective cohort studies and case reports) than in high quality studies (randomized clinical trial and meta-analysis). Conclusion Quality of research on drugs used for treating vascular anomalies in infants is poor and challenges rational drug use. Indeed, knowledge of drug treatment in VA relies mainly on research of poor methodological quality. Despite the use of drugs carrying a significant risk of adverse effects, drug safety is also poorly reported. This is alarming because some treatments, like antineoplastic agents and immunosuppressants, display an unsafe adverse effect profile. A publication bias towards positive results probably leads to overestimation of drug efficacy in vascular anomalies. An independent international pharmacovigilance system for drug use in vascular anomalies is proposed to improve efficacy and safety reporting and promote quality drug prescribing.
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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 | 0.037 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".