SynthesisBMJ open2026
Systematic review of prediction models and meta-analysis of risk factors for invasive fungal infection in children.
Synthesis in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Interpretable Machine Learning Model for Fungal Infection Prediction: A Real-World Study.Health care science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo review the application of prediction models and risk factors identified by prediction models for invasive fungal infection (IFI) in children, and assess model performance, methodological rigour and applicability.
designThis is a systematic review of diagnostic prediction models and a meta-analysis of the risk factors. This study was registered on PROSPERO and performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis and Prediction model risk of bias assessment tool. DATA SOURCES: PubMed, Embase (Ovid), Medline, Cochrane Library and four Chinese Databases were searched on 10 Mar 2025. ELIGIBILITY CRITERIA: We included original studies that developed diagnostic prediction models for IFI in children and excluded the informal records. DATA EXTRACTION AND SYNTHESIS: Odds ratio (OR) with 95% confidence interval (CI) was calculated for risk factors, and a random-effects meta-analysis was applied to factors reported in at least two studies. For prediction models, a descriptive analysis was conducted to summarise model characteristics, model performance and the risk of bias.
resultsNine studies were included from 4069 articles. Nine studies developed ten diagnostic prediction models, and logistic regression was the most commonly used method. The predictive performance showed an area under receiver operating curves (AUROC) ranging from 0.76 to 0.95, but meta-analysis of AUROC was not conducted due to heterogeneity. All studies were identified as having a high risk of bias in critical appraisal, particularly in the analysis, mainly due to the lack of validation, as well as the failure to appropriately evaluate model performance and overfitting. Only two of nine studies that developed prediction models used internal or external validation.
conclusionsLogistic regression is a common method for predicting IFI in children, although machine learning methods have been popular in prediction models. Our study identified all studies as high risk of bias. To reduce bias, studies should use calibration measures, internal and external validation more frequently, and consider shrinkage methods when developing models.
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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.