ArticleJournal of clinical tuberculosis and other mycobacterial diseases2018
Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA
Article in Journal of clinical tuberculosis and other mycobacterial diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults.BMJ open · 2021Pooled it
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients.iScience · 2023Article
- Determination of Seroprevalence of Contagious Caprine Pleuropneumonia and Associated Risk Factors in Goats and Sheep Using Classification and Regression Tree.Animals : an open access journal from MDPI · 2021Article
- Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10-Year Retrospective Study.Journal of veterinary internal medicineArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRifampin-based therapy potentially exacerbates glycemic control among TB patients who are already at high risk of hyperglycemia. This impacts negatively to the optimal care of TB- diabetes mellitus co-affected patients. Classification and regression tree (CART), a machine-learning algorithm impervious to statistical assumptions is one of the ideal tools for clinical decision-making that can be used to identify hemoglobin A1C (HbA
methods340TB smear positive patients attending two peri-urban clinics were recruited and prospectively followed up for six months. Baseline HbA
resultsOf the 340 patients enrolled: 84%were cured, 7% completed therapy and 9% had unfavorable outcomes out of which 4% (n = 32) had microbiologic failure. Using CART HbA
conclusionUsing fractal geometry relationships to drug pharmacokinetics, low weight has profound influence on failure of anti-tuberculosis treatment among patients at risk for diabetes mellitus.
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