ArticleeLife2021
A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Azithromycin for Bacterial Watery Diarrhea: A Reanalysis of the AntiBiotics for Children With Severe Diarrhea (ABCD) Trial Incorporating Molecular Diagnostics.The Journal of infectious diseases · 2024Trial
- Improving Antibiotic Stewardship for Diarrheal Disease With Probability-Based Electronic Clinical Decision Support: A Randomized Crossover Trial.JAMA pediatrics · 2022Trial
- Performance of fecal inflammatory biomarkers to identify watery shigellosis: Findings from the Enterics for Global Health (EFGH) Shigella surveillance study.PLoS neglected tropical diseases · 2026Article
- Derivation and validation of clinical prediction models for viral etiologies of acute diarrhea in North American children presenting for emergency care.medRxiv : the preprint server for health sciences · 2026Article
- Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.Scientific reports · 2025Article
- Assessment and comparison of model estimated and directly observed weather data for prediction of diarrhoea aetiology.Epidemiology and infection · 2024Article
- Comprehensive diagnostic testing identifies diverse aetiologies of acute febrile illness among hospitalised children and adults in Sri Lanka: a prospective cohort study.BMJ public health · 2023Article
- Assessment of Model Estimated and Directly Observed Weather Data for Etiological Prediction of Diarrhea.medRxiv : the preprint server for health sciences · 2023Article
- Enteric Pathogen Testing Importance for Children with Acute Gastroenteritis: a Modified Delphi Study.Microbiology spectrum · 2022Article
- Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test' epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.