Safety Methodology in Pediatric Psychopharmacology Trials
Bibliographic record
Abstract
In recent years, there has been an increase in pediatric clinical trials as the result of an identified need for greater research with this population. Given the potential risks, and the vulnerability of the population, there has also been an identified need for greater safety elicitation and monitoring in pediatric psychopharmacology trials, for example, through the use of a data and safety monitoring board (DSMB). However, research indicates that pediatric trials and psychiatric trials are less likely to use a DSMB. The rationale for the current study was to determine what safety methodologies have been reported in pediatric psychopharmacology trials over the past 10 years. A literature review was conducted of all pediatric psychopharmacology trials published since 2001. Results indicated that the most common elicitation method was collecting laboratory information and vital signs. Six percent of trials solely relied on spontaneous reporting of adverse events, and only 11.8% reported using a DSMB. These results suggest that elicitation methods and use of DSMBs are still low. Practical considerations, affected stakeholders, and barriers are discussed. Recommendations for moving forward include the use of multiple elicitation methods and automatic requirement of a DSMB for pediatric psychopharmacology trials, required completion of a standardized safety reporting form, and engaging multiple interested parties in these processes.
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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 | 0.160 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".