ArticleiScience2024
Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 16 citations in OpenAlex.
- New Strategies in Tuberculosis Prediction: TB-CAST, an Explainable Baseline Score for Two-Month Sputum Culture Positivity.Life (Basel, Switzerland) · 2026Article
- Development and validation of a LASSO-derived nomogram for predicting unfavorable treatment outcomes in drug-resistant pulmonary tuberculosis.BMC infectious diseases · 2026Article
- Divergent Evolution of Tuberculosis Lesions During Treatment: A Longitudinal CT-Based Analysis of Progression and Regression Patterns.Diagnostics (Basel, Switzerland) · 2026Article
- Interpretable multimodal machine learning for diagnosis of drug-resistant tuberculosis.Frontiers in digital health · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- Analysis of High-Risk Factors for Tuberculosis Retreatment Based on Machine Learning and Latent Class Analysis.Infection and drug resistance · 2026Article
- Review
- Predictors of drug-resistant TB outcomes: Body mass index, HIV, and comorbidities.African journal of primary health care & family medicine · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- The Changing Landscape of Antibiotic Treatment: Reevaluating Treatment Length in the Age of New Agents.Antibiotics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens
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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.