Observational studyBMJ paediatrics open2025
Prediction of recurrent febrile seizures risk during the same febrile illness in children at a single tertiary centre in Turkiye.
Observational study in BMJ paediatrics open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- An interpretable machine learning model for predicting febrile seizures following enterovirus infection in children.Annals of medicine · 2026Article
- Summary of Best Evidence for the Management and Recurrence Prevention of Febrile Seizures in Children.Journal of multidisciplinary healthcare · 2026Review
- Pediatrics advances in 2024: choices in allergy, cardiology, critical care, endocrinology, gastroenterology, immunology, infectious diseases, neonatology, nephrology, neurology, nutrition, palliative care respiratory tract illnesses, and social media.Italian journal of pediatrics · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to develop a risk prediction model based on association rule mining to predict recurrent febrile seizures (RFS).
methodsThis is a retrospective observational study that examined the medical records of 105 children who were followed up with febrile seizure (FS) in a tertiary paediatric emergency department between October 2022 and December 2023. Children were divided into RFS and simple FS groups. RFS was defined as seizures occurring more than once within 24 hours of the first FS in the same febrile illness. Risk factors associated with RFS were determined by univariate and multivariate analyses. χ
resultsRFS was detected in 32 out of 105 cases with FS (30.5%). Potential risk factors contributing to the development of RFS were seizure duration, number of recurrent seizures, family history, body temperature, time from fever onset to seizure, time from seizure onset to arrival at the emergency department, hyponatraemia, osmotic pressure and low haemoglobin level. The CBA algorithm obtained a total of 11 classification rules for the two patient groups. Additionally, the cut-off values obtained from CBA and ROC analysis showed satisfactory consistency. The CBA model achieved 97% overall accuracy classification performance.
conclusionThe developed CBA model shows good predictive ability for RFS. The relevant model can be used as a risk estimation tool to identify children at risk of developing RFS.
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