Fixed-dose combination antituberculosis therapy: a systematic review and meta-analysis
Bibliographic record
Abstract
Fixed-dose combination (FDC) formulations are currently recommended for the treatment of active tuberculosis (TB). We have conducted a systematic review to evaluate the risk of treatment failure or disease relapse, acquired drug resistance, bacterial conversion after 2 months of treatment, adverse events, adherence and treatment satisfaction associated with treatment of active TB using FDC or separate drug formulations. We searched four electronic databases for randomised controlled trials and cohort studies. Results from trials that directly compared FDC to separate drug formulations were pooled. Results from other studies were reported separately. We identified 2450 citations from which 15 controlled trials and four additional relevant studies were included. In the 15 trials there were no differences in acquired drug resistance, bacterial conversion after 2 months of treatment or adverse drug reactions with FDC or separate drug formulations. There was a trend toward higher risk of failure or relapse with FDC (pooled relative risk 1.28 (95% CI 0.99-1.7)). Based on individual study results, only one of two trials that assessed treatment satisfaction, and none of five that assessed patient adherence, favoured FDCs. Although FDC formulations simplify TB therapy, the current evidence does not indicate that these formulations improve treatment outcomes among patients with active TB.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".