Implementing the 4R and 9H regimens for TB preventive treatment in Indonesia
Bibliographic record
Abstract
BACKGROUND: The implementation of tuberculosis preventive treatment (TPT) is challenging especially in resource-limited settings. As part of a Phase 3 trial on TPT, we described our experience with the use of rifampicin for 4 months (4R) and isoniazid for 9 months (9H) in Indonesia.METHODS: In 2011–2017, children and adults with latent TB infection were randomised to either 4R or 9H and followed until 16 months after randomisation for children and 28 months for adults. The primary outcome was the treatment completion rate. Secondary outcomes were Grade 3–5 adverse events (AEs), active TB occurrence, and health costs.RESULTS: A total of 157 children and 860 adults were enrolled. The 4R treatment completion rate was significantly higher than that of 9H (78.7% vs. 65.5%), for a rate difference of 13.2% (95% CI 7.1–19.2). No Grade 3–5 AEs were reported in children; in adults, it was lower in 4R (0.4%) compared to 9H (2.8%). The incidence of active TB was lower with 4R than with 9H (0.09/100 person-year vs. 0.36/100 person-year) (rate difference: –0.36/100 person-year). The total cost per patient was lower for the 4R regimen than for the 9H regimen (USD151.9 vs. USD179.4 in adults and USD152.9 vs. USD206.5 in children)CONCLUSIONS: Completion and efficacy rates for 4R were better than for 9H. Compared to 9H, 4R was cheaper in all age groups, safer in adults and equally safe in children. The Indonesian TB program could benefit from these benefits of the 4R regimen.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".