Publication Trends of Pediatric and Adult Randomized Controlled Trials in General Medical Journals, 2005–2018: A Citation Analysis
Bibliographic record
Abstract
Policy has been developed to promote the conduct of high-quality pediatric randomized controlled trials (RCTs). Whether these strategies have influenced publication trends in high-impact journals is unknown. We aim to evaluate characteristics, citation patterns, and publication trends of pediatric RCTs published in general medical journals (GMJs) compared with adult RCTs over a 13-year period. Studies were identified using Medline, and impact metrics were collected from Web of Science and Scopus. All RCTs published from 2005–2018 in 7 GMJs with the highest impact factors were identified for analysis. A random sample of matched pediatric and adult RCTs were assessed for publication characteristics, academic and non-academic citation. Citations were counted from publication until June 2019. Among 4146 RCTs, 2794 (67.3%) enrolled adults, 591 (14.2%) enrolled children, and 761 RCTs (18.3%) enrolled adult and pediatric patients. Adult RCTs published in GMJs grew by 5.1 publications per year (95% CI: 3.3–6.9), while the number of pediatric RCTs did not show significant change (−0.4 RCTs/year, 95% CI: −1.4–0.6). Adult RCTs were cited more than pediatric RCTs (median(IQR): 29.9 (68.5–462.8) citations/year vs. 13.2 (6.8–24.9) citations/year; p < 0.001); however, social media attention was similar (median(IQR) Altmetric Attention Score: 37 (13.75–133.8) vs. 26 (6.2–107.5); p = 0.25). Despite policies which may facilitate conduct of pediatric RCTs, the publishing gap in high-impact GMJs is widening.
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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 | 0.044 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.051 | 0.076 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".