Pediatric Analgesic Clinical Trial Designs, Measures, and Extrapolation: Report of an FDA Scientific Workshop
Bibliographic record
Abstract
Analgesic trials pose unique scientific, ethical, and practical challenges in pediatrics. Participants in a scientific workshop sponsored by the US Food and Drug Administration developed consensus on aspects of pediatric analgesic clinical trial design. The standard parallel-placebo analgesic trial design commonly used for adults has ethical and practical difficulties in pediatrics, due to the likelihood of subjects experiencing pain for extended periods of time. Immediate-rescue designs using opioid-sparing, rather than pain scores, as a primary outcome measure have been successfully used in pediatric analgesic efficacy trials. These designs maintain some of the scientific benefits of blinding, with some ethical and practical advantages over traditional designs. Preferred outcome measures were recommended for each age group. Acute pain trials are feasible for children undergoing surgery. Pharmacodynamic responses to opioids, local anesthetics, acetaminophen, and nonsteroidal antiinflammatory drugs appear substantially mature by age 2 years. There is currently no clear evidence for analgesic efficacy of acetaminophen or nonsteroidal antiinflammatory drugs in neonates or infants younger than 3 months of age. Small sample designs, including cross-over trials and N of 1 trials, for particular pediatric chronic pain conditions and for studies of pain and irritability in pediatric palliative care should be considered. Pediatric analgesic trials can be improved by using innovative study designs and outcome measures specific for children. Multicenter consortia will help to facilitate adequately powered pediatric analgesic trials.
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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 | 0.599 | 0.467 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".