A scoping review of interventions to prevent and treat adverse events during treatment of rifampin-susceptible tuberculosis
Bibliographic record
Abstract
BACKGROUND: Treatment-related adverse events are one of the leading barriers to tuberculosis treatment completion but have not been the focus of late-phase clinical trials. We performed a scoping review to identify interventions to improve the safety and tolerability of rifampin-susceptible tuberculosis. Our objective was to determine what interventions have been evaluated to prevent or manage adverse events, as well as what research is underway. METHODS AND FINDINGS: We searched Embase, PubMed, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from 1970 to December 2024 using a broad set of terms regarding adverse events, as well as citation searches to identify additional studies in topic areas that were not well-represented in the initial title search. To identify research in progress we searched Clintrials.gov, Cochrane reviews, and International Clinical Trials Registry Platform for trials reported to be active between January 2015 to April 2025. Of 7314 titles reviewed, 119 papers were available and eligible for this scoping review: 37 (31%) evaluated changes in the tuberculosis treatment regimen, 55 (46%) evaluated other interventions to prevent adverse events, and 27 (23%) evaluated treatment of adverse events. Only 7 studies reported enrollment of children < 12 years old. Of the 49 clinical trials, 20 (41%) had sample sizes < 50 participants/arm. Notable gaps in research in this field: uncertainty about the safety of pyrazinamide, lack of research on prevention and management of nausea/vomiting, uncertainty about the impact of hepatoprotectants, and lack of inclusion of children. Of the 8 study proposals that appear to be in progress, five were for a single topic: isoniazid dosing based on N-actyltransferase-2 status. CONCLUSIONS: There has been considerable research on improving the safety and tolerability of tuberculosis treatment, but its impact is limited by under-powered studies, the lack of inclusion of key subgroups, and important gaps in the research portfolio (uncertainties about the safety of pyrazinamide and the efficacy of hepatoprotectants, lack of research on ways to manage and prevent treatment-related nausea). It is concerning that the research pipeline for interventions to improve safety and tolerability appears to be quite limited Our review has identified promising interventions that may make treatment better tolerated, and hence, more effective.
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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 | 0.039 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".