Empirical Evaluation of Age Groups and Age-Subgroup Analyses in Pediatric Randomized Trials and Pediatric Meta-analyses
Bibliographic record
Abstract
BACKGROUND: An important step toward improvement of the conduct of pediatric clinical research is the standardization of the ages of children to be included in pediatric trials and the optimal age-subgroups to be analyzed. METHODS: We set out to evaluate empirically the age ranges of children, and age-subgroup analyses thereof, reported in recent pediatric randomized clinical trials (RCTs) and meta-analyses. First, we screened 24 RCTs published in Pediatrics during the first 6 months of 2011; second, we screened 188 pediatric RCTs published in 2007 in the Cochrane Central Register of Controlled Trials; third, we screened 48 pediatric meta-analyses published in the Cochrane Database of Systematic Reviews in 2011. We extracted information on age ranges and age-subgroups considered and age-subgroup differences reported. RESULTS: The age range of children in RCTs published in Pediatrics varied from 0.1 to 17.5 years (median age: 5; interquartile range: 1.8-10.2) and only 25% of those presented age-subgroup analyses. Large variability was also detected for age ranges in 188 RCTs from the Cochrane Central Register of Controlled Trials, and only 28 of those analyzed age-subgroups. Moreover, only 11 of 48 meta-analyses had age-subgroup analyses, and in 6 of those, only different studies were included. Furthermore, most of these observed differences were not beyond chance. CONCLUSIONS: We observed large variability in the age ranges and age-subgroups of children included in recent pediatric trials and meta-analyses. Despite the limited available data, some age-subgroup differences were noted. The rationale for the selection of particular age-subgroups deserves further study.
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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 | 0.238 | 0.482 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".