ReviewFrontiers in microbiology2023
Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine.
Review in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Biopsychosocial determinants of tuberculosis care and treatment outcomes: a scoping review and bibliometric analysis.Frontiers in cellular and infection microbiology · 2026Pooled it
- CYP2E1 and CYP2D6 in Anti-Tuberculosis Drug-Induced Liver Injury: Mechanisms, Epigenetic Regulation, and Translational Implications.Clinical and translational science · 2026Review
- Implementation of digital tuberculosis information systems: perspectives from 10 high TB burden countries.BMC infectious diseases · 2026Article
- Challenges and Opportunities for Improved Tuberculosis and HIV Prevention in South America.Open forum infectious diseases · 2026Review
- Reimagining tuberculosis elimination in India: diagnostics, drug resistance, and digital health strategies.Frontiers in epidemiology · 2026Review
- Artificial intelligence for tuberculosis management in Africa: opportunities, challenges, and implementation.Frontiers in public health · 2026Review
- Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment.Journal of medical Internet research · 2025Article
- Article
- Advancing against drug-resistant tuberculosis: an extensive review, novel strategies and patent landscape.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Xpert MTB/RIF Ultra Ct Value: A Quick Indicator of Sputum Bacillary Load and Smear Status Prediction in Individuals with Pulmonary Tuberculosis.The East African health research journal · 2025Article
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
- The Next Frontier in Tuberculosis Investigation: Automated Whole Genome Sequencing forInternational journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.
Indexed as
Identifiers
What Socratic 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 Socratic graph.