Analysis of Pediatric Clinical Drug Trials for Neuropsychiatric Conditions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Neuropsychiatric conditions represent a large and increasing disease burden in children. A number of drugs are available for the treatment of these conditions, but most drugs have not been adequately tested in children, and off-label drug use remains widespread. We sought to define and quantify recent and ongoing clinical research on the use of neuropsychiatric drugs in children. METHODS: Drug trials registered in ClinicalTrials.gov between 2006 and 2011 and studying neuropsychiatric conditions were selected and classified based on the drug's Food and Drug Administration (FDA) approval status in children. We measured the proportion of trials seeking to expand the use of a drug to pediatric patients and the proportion of available drugs studied in children. RESULTS: Only 10% of neuropsychiatric trials focused on children. Of 303 drugs studied in both pediatric and adult populations, 90% lacked FDA approval in children and 97% were not approved in children for the indication studied. However, only 19% of all neuropsychiatric drugs were under study in pediatric populations, with as few as 8% of either antidepressant or antipsychotic drugs. Overall, 76% of pediatric drug trials examined a drug previously unapproved in children and 26% explored the use of a drug for a new indication. CONCLUSIONS: Despite the rising prevalence of neuropsychiatric disease and the paucity of FDA-approved pediatric drugs, only a small proportion of trials focus on pediatric populations and these trials cover only a fraction of available drugs. This deficiency is most pronounced for depression and schizophrenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".