Predictors of treatment outcomes in multi-drug-resistant tuberculosis in India (2015–2025): A systematic review.
Bibliographic record
Abstract
Background: Multi-drug-resistant tuberculosis (MDR-TB) remains a major public health concern in India, with treatment outcomes often falling below global targets. Identifying predictors of treatment success or failure is critical for improving care and informing national strategies. Objectives: To systematically review and synthesize the evidence on predictors of treatment outcomes in MDR-TB patients in India between 2015 and 2025. Materials and Methods: Electronic databases (PubMed, Scopus, Google Scholar) were searched from January 2015 to May 2025. Additional articles were identified through manual reference screening. The review included observational studies on Indian patients receiving MDR-TB treatment under programmatic or hospital-based settings. Risk of bias was assessed using the Newcastle-Ottawa Scale. Data were categorized thematically into demographic, clinical, comorbidity, and treatment-related predictors. Results: Seven studies were included, with sample sizes ranging from 95 to over 2,000 patients. Common predictors of unfavorable outcomes included older age, male sex, undernutrition (low BMI/albumin), HIV co-infection, substance use (alcohol/smoking), poor adherence, and adverse drug reactions. Treatment success rates were generally below 50%. Conclusions and Implications: Multiple modifiable and non-modifiable factors contribute to poor MDR-TB outcomes in India. Addressing undernutrition, supporting adherence, and managing comorbidities like HIV and substance abuse can improve outcomes. These findings can inform targeted interventions under the National TB Elimination Programme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".