A Systematic Review and Meta-Analysis of Tuberculous Preventative Therapy Adverse Events
Bibliographic record
Abstract
BACKGROUND: Tuberculosis preventative therapy (TPT) is a key part of the World Health Organization's (WHO) end tuberculosis (TB) strategy. However, the occurrence of potentially serious adverse events (AE) is a limitation of TPT regimens. We conducted a systemic review and meta-analysis to estimate the incidence of AE and hepatotoxicity with various TPT regimens to help inform clinical decision making. METHODS: We searched MEDLINE, Cochrane, Health Star, and EMBASE from 1952 to April 2021 for studies reporting AE associated with TPT. Included studies reported AE stratified by regimen and provided the number of participants receiving each regimen. We used a random-effect model to meta-analyze the cumulative incidence of AE. RESULTS: We included 175 publications describing TPT-related AE in 277 cohorts. Among adults, the incidence of any AE, and hepatotoxicity leading to drug discontinuation was 3.7% and 1.1%, respectively, compared to 0.4% and 0.02%, respectively, in children. The highest incidence of any AE, and AE leading to drug discontinuation was with 3 months isoniazid and rifapentine (3HP), and the lowest was with 4 months rifampin (4R). 4R also had the lowest incidence of hepato-toxic AE and drug discontinuation due to hepato-toxic AE. 3HP also had a low incidence of hepato-toxic AE. CONCLUSIONS: Although our study was limited by variability in methods and quality of AE reporting in the studies reviewed, pediatric populations had a very low incidence of AE with all TPT regimens reviewed. In adults, compared to mono-H regimens all rifamycin-based regimens were safer, although 4R had the lowest incidence of TPT-related AE of all types and of hepatotoxicity.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".