Aminoglycosides and Capreomycin in the Treatment of Multidrug-resistant Tuberculosis: Individual Patient Data Meta-analysis of 12 030 Patients From 25 Countries, 2009–2016
Bibliographic record
Abstract
BACKGROUND: As new drugs are developed for multidrug-resistant tuberculosis (MDR-TB), the role of currently used drugs must be reevaluated. METHODS: We combined individual-level data on patients with pulmonary MDR-TB published during 2009-2016 from 25 countries. We compared patients receiving each of the injectable drugs and those receiving no injectable drugs. Analyses were based on patients whose isolates were susceptible to the drug they received. Using random-effects logistic regression with propensity score matching, we estimated the effect of each agent in terms of standardized treatment outcomes. RESULTS: More patients received kanamycin (n = 4330) and capreomycin (n = 2401) than amikacin (n = 2275) or streptomycin (n = 1554), opposite to their apparent effectiveness. Compared with kanamycin, amikacin was associated with 6 more cures per 100 patients (95% confidence interval [CI], 4-8), while streptomycin was associated with 7 (95% CI, 5-8) more cures and 5 (95% CI, 4-7) fewer deaths per 100 patients. Compared with capreomycin, amikacin was associated with 9 (95% CI, 6-11) more cures and 5 (95% CI, 2-8) fewer deaths per 100 patients, while streptomycin was associated with 10 (95% CI, 8-13) more cures and 10 (95% CI, 7-12) fewer deaths per 100 patients treated. In contrast to amikacin and streptomycin, patients treated with kanamycin or capreomycin did not fare better than patients treated with no injectable drugs. CONCLUSIONS: When aminoglycosides are used to treat MDR-TB and drug susceptibility test results support their use, streptomycin and amikacin, not kanamycin or capreomycin, are the drugs of choice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".