ArticleOpen forum infectious diseases2026
Analysis of Drug Resistance Characteristics and Risk Factors of Cavitary Tuberculosis Based on Whole Genome Sequencing and Machine Learning.
Article in Open forum infectious diseases, 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pulmonary cavitation, a high-burden driver of pulmonary tuberculosis (PTB) transmission, necessitates targeted interventions. This study comprehensively analyzes the drug-resistance profiles and risk factors associated with cavitary TB to inform precise prevention and control strategies. Methods: This study analyzed 1247 cavitary PTB patients from Guangxi (2020-2024) with complete strain and clinical data. Whole-genome sequencing (WGS) characterized strain lineages and drug resistance. Key predictors were selected using Lasso regression, and the optimal model was identified from 9 machine learning (ML) models based on AUC, Shapley Additive Explanations (SHAP) elucidated feature contributions to severe cavitary TB risk. Results: Lineages were primarily classified as Lineage 2 (65.20%), Lineage 4 (29.91%), and 35 cases of mixed infections (2.81%). Concordance between WGS and phenotypic drug susceptibility testing was moderate for Isoniazid (INH) ( Conclusions: The rpoB_p.Ser450 mutation is linked to severe cavitary PTB, but clinical and population studies are still needed to confirm this association. Therefore, new tools based on clinical indicators and biomarkers are needed to achieve early warning and timely intervention for the risk of severe cavitation.
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.