Treatment Outcomes of Multidrug-Resistant Tuberculosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Treatment outcomes for multidrug-resistant Mycobacterium Tuberculosis (MDRTB) are generally poor compared to drug sensitive disease. We sought to estimate treatment outcomes and identify risk factors associated with poor outcomes in patients with MDRTB. METHODOLOGY/PRINCIPAL FINDINGS: We performed a systematic search (to December 2008) to identify trials describing outcomes of patients treated for MDRTB. We pooled appropriate data to estimate WHO-defined outcomes at the end of treatment and follow-up. Where appropriate, pooled covariates were analyzed to identify factors associated with worse outcomes. Among articles identified, 36 met our inclusion criteria, representing 31 treatment programmes from 21 countries. In a pooled analysis, 62% [95% CI 57-67] of patients had successful outcomes, while 13% [9]-[17] defaulted, 11% [9]-[13] died, and 2% [1]-[4] were transferred out. Factors associated with worse outcome included male gender 0.61 (OR for successful outcome) [0.46-0.82], alcohol abuse 0.49 [0.39-0.63], low BMI 0.41[0.23-0.72], smear positivity at diagnosis 0.53 [0.31-0.91], fluoroquinolone resistance 0.45 [0.22-0.91] and the presence of an XDR resistance pattern 0.57 [0.41-0.80]. Factors associated with successful outcome were surgical intervention 1.91 [1.44-2.53], no previous treatment 1.42 [1.05-1.94], and fluoroquinolone use 2.20 [1.19-4.09]. CONCLUSIONS/SIGNIFICANCE: We have identified several factors associated with poor outcomes where interventions may be targeted. In addition, we have identified high rates of default, which likely contributes to the development and spread of MDRTB.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".