Pharmacoepidemiological safety studies in children: a systematic review
Bibliographic record
Abstract
PURPOSE: In order to identify challenges in pediatric pharmacoepidemiological safety studies, we assessed the characteristics of such (published) studies. METHODS: Relevant articles from inception to 2013 were retrieved from Embase and Medline. We sequentially screened titles, abstracts and full texts with independent validation. We systematically collected data regarding general information, study methods and results. RESULTS: Out of 4825 unique articles, 268 full texts (5.6%) were retained; 147 (54.9%) pertained to drugs rather than vaccines. Considering the 268 studies, 202 (75.4%) concerned children and adolescents (2 to 11 years) and 14 (5.3%) included preterm newborns. Most studies originated from North America (154 [57.5%]) or Europe (92 [34.3%]). Only 47 studies (17.5%) were privately funded. The majority (174 [64.9%]) were cohort studies. Out of 268 studies, 196 (73.1%) collected data retrospectively; paper medical charts were the most common data source for the exposures (85 [31.7%]) and outcomes (122 [45.5%]). Only 3 (2.0%) drug-only studies investigated rarely used drugs. Considering all 268 studies, only 27 (10.1%) reported sample size or power calculation. Most (75 [51.0%]) drug-only studies corrected confounding by multivariate modeling unlike stratification in 66 (55.9%) vaccine-only studies. Considering 75 child-only studies without any statistically significant result, 41 (54.7%) did not discuss lack of power. CONCLUSIONS: Although the field of pediatric pharmacoepidemiology is steadily developing evaluation seldom includes neonates, is mainly focused on few drug classes and safety outcomes and concerns mainly drug use in developed countries. Small study size is a specific challenge in pediatrics. Reporting should be improved. © 2016 The Authors. Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".