ArticleFrontiers in medicine2025
Validation of a novel EC and PPD-based decision tree model for tuberculosis screening in Tibetan adolescent students.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To evaluate the utility and effectiveness of the recombinant Methods: A total of 1,047 primary and secondary school students in Seda County were enrolled. Both the tuberculin skin test (TST/PPD) and EC skin test were administered to all participants. Data analysis was performed using R 4.3.0 and Python 12.0 statistical software. Descriptive analyses included skewed continuous data expressed as median (Q₁, Q₃) and analyzed using the Kruskal-Wallis test, while categorical data were presented as n (%) and analyzed using Chi-square or Fisher's exact tests. Model construction and performance evaluation were implemented in Python, utilizing packages such as graphviz, matplotlib, and scikit-plot for visualization and metrics calculation. Results: Based on expert consensus, participants were stratified into three groups: BCG vaccination ( Conclusion: While the EC skin test exhibits 100% sensitivity for latent TB infection, it cannot differentiate between persistent post-vaccination positivity and true uninfected status. The EC + PPD decision tree model synergistically optimizes multi-dimensional metrics, enabling high-sensitivity detection of latent infections and precise exclusion of false positives, thereby improving overall diagnostic performance. This integrated approach could improve TB screening accuracy in high-altitude student populations, inform targeted public health interventions, and warrants further validation. While this study was conducted in a high-altitude region, the combined EC + PPD approach warrants evaluation in other settings with high BCG vaccination rates.
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