Paediatric pharmacotherapy and drug regulation: Moving past the therapeutic orphan
Bibliographic record
Abstract
The development of specific drug therapy for children was a paradigm-changing event that transformed paediatric medical practice. However, a series of tragedies involving drug treatment for children resulted in a gap developing between drug regulation and practice, with the majority of drugs used in child healthcare being used off-label, rendering children therapeutic orphans. Over the past two decades changes in drug regulation led by the US Food and Drug Administration and followed by the European Union's European Medicines Agency have led to substantial changes in how new drugs with potential use in children are studied and labelled. While these changes have substantially improved labelling for new drugs, there has been much less progress with older drugs. Although the unique challenges of conducting clinical research in children have been addressed by novel clinical trial designs, many of these innovations have not been translated into approaches accepted for the drug approval process. The regulations applying to the need for paediatric studies currently are only applicable in the United States and the European Union, and there is less impetus for paediatric labelling in other jurisdictions. This impacts on a number of issues beyond labelling, including the availability of child-friendly formulations. Finally, the impact of Brexit on paediatric drug studies in the UK remains unclear and is subject to ongoing negotiations between the UK government and the European Union.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".