Clinical trial designs and models for analgesic medications for acute pain in neonates, infants, toddlers, children, and adolescents: ACTTION recommendations
Bibliographic record
Abstract
Clinical trials to test the safety and efficacy of analgesics across all pediatric age cohorts are needed to avoid inappropriate extrapolation of adult data to children. However, the selection of acute pain models and trial design attributes to maximize assay sensitivity, by pediatric age cohort, remains problematic. Acute pain models used for drug treatment trials in adults are not directly applicable to the pediatric age cohorts-neonates, infants, toddlers, children, and adolescents. Developmental maturation of metabolic enzymes in infants and children must be taken into consideration when designing trials to test analgesic treatments for acute pain. Assessment tools based on the levels of cognitive maturation and behavioral repertoire must be selected as outcome measures. Models and designs of clinical trials of analgesic medications used in the treatment of acute pain in neonates, infants, toddlers, children, and adolescents were reviewed and discussed at an Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) Pediatric Pain Research Consortium consensus meeting. Based on extensive reviews and continuing discussions, the authors recommend a number of acute pain clinical trial models and design attributes that have the potential to improve the study of analgesic medications in pediatric populations. Recommendations are also provided regarding additional research needed to support the use of other acute pain models across pediatric age cohorts.
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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 | 0.243 | 0.365 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".