Do systematic reviews on pediatric topics need special methodological considerations?
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are key tools to enable decision making by healthcare providers and policymakers. Despite the availability of the evidence based Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA-2009 and PRISMA-P 2015) statements that were developed to improve the transparency and quality of reporting of systematic reviews, uncertainty on how to deal with pediatric-specific methodological challenges of systematic reviews impairs decision-making in child health. In this paper, we identify methodological challenges specific to the design, conduct and reporting of pediatric systematic reviews, and propose a process to address these challenges. DISCUSSION: One fundamental decision at the outset of a systematic review is whether to focus on a pediatric population only, or to include both adult and pediatric populations. Both from the policy and patient care point of view, the appropriateness of interventions and comparators administered to pre-defined pediatric age subgroup is critical. Decisions need to be based on the biological plausibility of differences in treatment effects across the developmental trajectory in children. Synthesis of evidence from different trials is often impaired by the use of outcomes and measurement instruments that differ between trials and are neither relevant nor validated in the pediatric population. Other issues specific to pediatric systematic reviews include lack of pediatric-sensitive search strategies and inconsistent choices of pediatric age subgroups in meta-analyses. In addition to these methodological issues generic to all pediatric systematic reviews, special considerations are required for reviews of health care interventions' safety and efficacy in neonatology, global health, comparative effectiveness interventions and individual participant data meta-analyses. To date, there is no standard approach available to overcome this problem. We propose to develop a consensus-based checklist of essential items which researchers should consider when they are planning (PRISMA-PC-Protocol for Children) or reporting (PRISMA-C-reporting for Children) a pediatric systematic review. Available guidelines including PRISMA do not cover the complexity associated with the conduct and reporting of systematic reviews in the pediatric population; they require additional and modified standards for reporting items. Such guidance will facilitate the translation of knowledge from the literature to bedside care and policy, thereby enhancing delivery of care and improving child health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.189 | 0.621 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.015 |
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; both teacher heads 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".