Three months of weekly rifapentine plus isoniazid is less hepatotoxic than nine months of daily isoniazid for LTBI
Bibliographic record
Abstract
SETTING: Nine months of daily isoniazid (9H) and 3 months of once-weekly rifapentine plus isoniazid (3HP) are recommended treatments for latent tuberculous infection (LTBI). The risk profile for 3HP and the contribution of hepatitis C virus (HCV) infection to hepatotoxicity are unclear. OBJECTIVES: To evaluate the hepatotoxicity risk associated with 3HP compared to 9H, and factors associated with hepatotoxicity. DESIGN: Hepatotoxicity was defined as aspartate aminotransferase (AST) >3 times the upper limit of normal (ULN) with symptoms (nausea, vomiting, jaundice, or fatigue), or AST >5 x ULN. We analyzed risk factors among adults who took at least 1 dose of their assigned treatment. A nested case-control study assessed the role of HCV. RESULTS: Of 6862 participants, 77 (1.1%) developed hepatotoxicity; 52 (0.8%) were symptomatic; 1.8% (61/3317) were on 9H and 0.4% (15/3545) were on 3HP (P < 0.0001). Risk factors for hepatotoxicity were age, female sex, white race, non-Hispanic ethnicity, decreased body mass index, elevated baseline AST, and 9H. In the case-control study, HCV infection was associated with hepatotoxicity when controlling for other factors. CONCLUSION: The risk of hepatotoxicity during LTBI treatment with 3HP was lower than the risk with 9H. HCV and elevated baseline AST were risk factors for hepatotoxicity. For persons with these risk factors, 3HP may be preferred.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".