Efficacy of bedaquiline in the treatment of drug-resistant tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Drug-resistant tuberculosis (DR-TB) remains a major public health concern worldwide. Bedaquiline, a novel diarylquinoline, was added to the WHO-recommended all-oral regimen for patients with multidrug-resistant tuberculosis. We performed a systematic review and meta-analysis to determine the effect of bedaquiline on tuberculosis treatment outcomes. METHODS: We searched the PubMed, Web of Science and EMBASE databases for relevant studies published up to March 12, 2021. We included studies in which some participants received bedaquiline and others did not. Stata version 16.0 (Stata Corp., College Station, Texas, USA) was used to analyze the results of the meta-analysis. Risk ratios (RRs) with 95% confidence intervals (95% CIs) were calculated to evaluate the effect of bedaquiline on drug-resistant tuberculosis. Between-study heterogeneity was examined by the I-squared test. Randomized controlled trials were assessed for quality using the Jadad scale, and cohort studies were assessed using the Newcastle-Ottawa scale. RESULTS: Eight studies, including 2 randomized controlled trials and 6 cohort studies involving a total of 21,836 subjects, were included. When compared with the control, bedaquiline treatment was associated with higher rates of culture conversion (risk ratio (RR):1.272 (1.165-1.389), P < 0.001). We found substantial evidence of a significant reduction in all-cause death (RR: 0.529 (0.454-0.616), P < 0.001)) in the bedaquiline treatment group. There was no significant reduction in treatment success (RR = 0.980 (0.948-1.013, P = 0.234)). CONCLUSIONS: This study demonstrated that compared with patients who do not receive bedaquiline, this drug has the potential to achieve a higher culture conversion rate and a lower mortality risk among drug-resistant tuberculosis cases.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".