High-dose rifamycins in the treatment of TB: a systematic review and meta-analysis
Bibliographic record
Abstract
Background There is growing interest in using high-dose rifamycin (HDR) regimens in TB treatment, but the safety and efficacy of HDR regimens remain uncertain. We performed a systematic review and meta-analysis comparing HDR to standard-dose rifamycin (SDR) regimens. Methods We searched MEDLINE, Embase, CENTRAL, Cochrane Database of Systematic Reviews and clinicaltrials.gov for prospective studies comparing daily therapy with HDRs to SDRs. Rifamycins included rifampicin, rifapentine and rifabutin. Our primary outcome was the rate of severe adverse events (SAEs), with secondary outcomes of death, all adverse events, SAE by organ and efficacy outcomes of 2-month culture conversion and relapse. This study was prospectively registered in the International Prospective Register of Systematic Reviews (CRD42020142519). Results We identified 9057 articles and included 13 studies with 6168 participants contributing 7930 person-years (PY) of follow-up (HDR: 3535 participants, 4387 PY; SDR: 2633 participants, 3543 PY). We found no significant difference in the pooled incidence rate ratio (IRR) of SAE between HDR and SDR (IRR 1.00, 95% CI 0.82 to 1.23, I 2 =41%). There was no significant difference when analysis was limited to SAE possibly, probably or likely medication-related (IRR 1.07, 95% CI 0.82 to 1.41, I 2 =0%); studies with low risk of bias (IRR 0.98, 95% CI 0.79 to 1.20, I 2 =44%); or studies using rifampicin (IRR 1.00, 95% CI 0. 0.75–1.32, I 2 =38%). No significant differences were noted in pooled outcomes of death, 2-month culture conversion and relapse. Conclusions HDRs were not associated with a significant difference in SAEs, 2-month culture conversion or death. Further studies are required to identify specific groups who may benefit from HDR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".