ArticleInternational journal of clinical pharmacy2026
Risk prediction models for adverse drug reactions in pediatrics: a scoping review.
Article in International journal of clinical pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
introductionPredictive models play a critical role in enhancing medication safety in clinical practice. While multiple models for adverse drug reactions (ADRs) in adults have demonstrated potential clinical utility, pediatric-specific predictive models remain understudied, with limited comprehensive evaluation of their methodological quality and reporting standards.
aimTo map the landscape of existing risk prediction models for ADRs specifically developed or validated for pediatric patients, and to describe their characteristics, methodological quality, and reporting completeness.
methodA systematic search was conducted in Embase, PubMed, CNKI, Wanfang, VIP, and SinoMed for studies on pediatric ADR prediction models. The information from included studies was evaluated using the Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist, the risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST), and adherence to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines was reviewed. Predictive ability of the included model, including the area under the receiver operating characteristic curve (AuROC), sensitivity, and specificity, was also reported.
resultsOut of 12,667 screened studies, 7 articles (describing 10 models) met the inclusion criteria. Study designs included case-control, nested case-control, prospective cohort, and cross-sectional studies. Logistic regression was the primary modeling method, with one study using machine learning. Common methodological limitations included unreported handling of missing data and univariable predictor screening. Model discrimination (AuROC) ranged from 0.63-0.97, with sensitivity and specificity between 52.02-98.50% and 33.33-98.79%, respectively. TRIPOD adherence varied (62.16-86.49%), with notable reporting deficiencies in blinding, sample size justification, intervention details, model usage instructions, and supplementary materials. No models underwent external validation.
conclusionExisting pediatric ADR prediction models are limited by methodological and reporting shortcomings. Future research should focus on rigorous model development and external validation to ensure generalizability across diverse clinical settings.
Indexed as
Identifiers
41217606What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.