A systematic review of adverse events of rifapentine and isoniazid compared to other treatments for latent tuberculosis infection
Bibliographic record
Abstract
PURPOSE: Tuberculosis (TB) remains a common cause of death globally. A regimen of 12 doses of isoniazid (INH) and rifapentine given once weekly (INH/RPT-3) has recently been recommended by the World Health Organization for the treatment of latent TB infection (LTBI). We aimed to determine whether the INH/RPT-3 regimen had similar or lesser rates of adverse events compared to other LTBI regimens, namely INH for 9 months, INH for 6 months, rifampin for 3 to 4 months, and rifampin plus INH for 3 to 4 months. METHODS: We searched MEDLINE, Embase, CENTRAL, PubMed, ICTRP, clinicaltrials.gov, and Canadian Agency for Drugs and Technologies in Health's Gray Matters Light for randomized, postmarketing, and comparative nonrandomized studies of patients with confirmed LTBI that reported the frequency of at least 1 adverse event of relevance for a regimen of interest. The search included studies published until March 2017. The frequencies of adverse events were extracted and are presented descriptively. RESULTS: Data from 23 randomized and 55 nonrandomized studies were included. Although inconsistent event reporting and high heterogeneity limited comparisons, the adverse event profile of INH/RPT-3 appeared generally favorable. Flu-like reactions were reported with an increased frequency and hepatotoxicity with a lower frequency than standard treatment. CONCLUSIONS: While INH/RPT-3 had an overall low frequency of adverse events compared to INH monotherapy, reporting of adverse events for many regimens was limited meaning results should be interpreted cautiously. Future studies of LTBI treatment would benefit from more complete collection and reporting of adverse events and more consistent definitions 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".