Unlicensed and Off-Label Drug Use in Children Before and After Pediatric Governmental Initiatives
Bibliographic record
Abstract
OBJECTIVES: Governmental agencies (US Food and Drug Administration and European Medicines Agency) implemented initiatives to improve pediatric clinical research, starting in 1997 and 2007, respectively. The aim of this review was to quantify the unlicensed and off-label drug uses in children before and after these implementations. METHODS: Literature review of unlicensed and off-label drug uses was performed on PubMed and Google-Scholar from 1985 to 2014. Relevant titles/abstracts were reviewed, and articles were included if evaluating unlicensed/off-label drug uses, with a clear description of health care setting and studied population. Included articles were divided into 3 groups: studies conducted in United States (before/after 2007), in Europe (before/after 2007), and in other countries. RESULTS: Of the 48 articles reviewed, 27 were included. Before implementation of pediatric initiatives, global unlicensed drug use rate in Europe was found to be 0.2% to 36% for inpatients and 0.3% to 16.6% for outpatients. After implementation, it marginally decreased to 11.4% and 1.26% to 6.7%, respectively. Concerning off-label drug use rates, it was found to be 18% to 66% for inpatients and 10.5% to 37.5% for outpatients before the implementation. After implementation, it decreased marginally to 33.2% to 46.5% and to 3.3% to 13.5%, respectively. In other countries, unlicensed and off-label drug use rates were found to be, respectively, 8% to 27.3% and 11% to 47%. CONCLUSIONS: Governmental initiatives to improve clinical research conducted in children seem to have had a marginal effect to decrease the unlicensed and off-label drug uses prevalence in Europe.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".