Group 5 drugs for multidrug-resistant tuberculosis: individual patient data meta-analysis
Bibliographic record
Abstract
The role of so-called "group 5" second-line drugs as a part of antibiotic therapy for multidrug-resistant tuberculosis (MDR-TB) is widely debated. We performed an individual patient data meta-analysis to evaluate the effectiveness of several group 5 drugs including amoxicillin/clavulanic acid, thioacetazone, the macrolide antibiotics, linezolid, clofazimine and terizidone for treatment of patients with MDR-TB.Detailed individual patient data were obtained from 31 published cohort studies of MDR-TB therapy. Pooled treatment outcomes for each group 5 drug were calculated using a random effects meta-analysis. Primary analyses compared treatment success to a combined outcome of failure, relapse or death.Among 9282 included patients, 2191 received at least one group 5 drug. We found no improvement in treatment success among patients taking clofazimine, amoxicillin/clavulanic acid or macrolide antibiotics, despite applying a number of statistical approaches to control confounding. Thioacetazone was associated with increased treatment success (OR 2.6, 95% CI 1.1-6.1) when matched controls were selected from studies in which the group 5 drugs were not used at all, although this result was heavily influenced by a single study.The development of more effective antibiotics to treat drug-resistant TB remains an urgent priority.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".