Off-Label Use and Safety of Drug Use in Vascular Anomalies
Bibliographic record
Abstract
BACKGROUND: Off-label drug use is associated with an increased risk of adverse drug reactions. It is common in pediatrics and in rare diseases, which are two characteristics applying to vascular anomalies (VA). OBJECTIVES: The aim of this work was to quantify off-label drug use in VA and assess its safety. METHODS: A review was conducted to extract a list of drugs used in VA management. A drug was considered to have significant safety concerns if a black box warning was present or if a serious adverse drug reaction (SADR) was reported in at least 1% of the patients (SADR is defined as a noxious and unintended response to a drug that occurs at any dose and results in hospitalization, prolongation of existing hospitalization, congenital malformation, persistent or significant disability or incapacity, life-threatening condition, or death). The labelling status and safety of each drug was assessed based on the product monograph, Micromedex, and the FDA data. RESULTS: We found that 98.9% of the inventoried drugs were used off-label or unlicensed for VA management. Only the oral solution of propranolol hydrochloride (Hemangeol®) for the treatment of infantile hemangiomas is approved. Significant safety issues concerned 73% of the drugs and were more frequent among systemic than locally delivered drugs. CONCLUSIONS: Off-label drug use in VA is the rule and not the exception. Significant safety concerns are common. It is necessary to carefully weigh risk and benefits for every patient when using systemic and local treatments carrying safety concerns. Patients should be openly informed and involved in the decision-making process.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".