Long-Term Protective Effect of Tuberculosis Preventive Therapy in a Medium/High Tuberculosis Incidence Setting
Bibliographic record
Abstract
BACKGROUND: The duration of the protective effect of tuberculosis preventive therapy (TPT) is controversial. Some studies have found that the protective effect of TPT is lost after cessation of therapy among people with human immunodeficiency virus (HIV) in settings with very high tuberculosis incidence, but others have found long-term protection in low-incidence settings. METHODS: We estimated the incidence rate (IR) of new tuberculosis disease for up to 12 years after randomization to 4 months of rifampin or 9 months of isoniazid, among 991 Brazilian participants in a TPT trial in the state of Rio de Janeiro, with an incidence of 68.6/100 000 population in 2022. The adjusted hazard ratios (aHRs) of independent variables for incident tuberculosis were calculated. RESULTS: The overall tuberculosis IR was 1.7 (95% confidence interval [CI], 1.01- 2.7) per 1000 person-years (PY). The tuberculosis IR was higher among those who did not complete TPT than in those who did (2.9 [95% CI, 1.3-5.6] vs 1.1 [.4-2.3] per 1000 PY; IR ratio, 2.7 [1.0-7.2]). The tuberculosis IR was higher within 28 months after randomization (IR, 3.5 [95% CI, 1.6-6.6] vs 1.1 [.5-2.1] per 1000 PY between 28 and 143 months; IR ratio, 3.1 [1.2-8.2]). Treatment noncompletion was the only variable associated with incident tuberculosis (aHR, 3.2 [95% CI, 1.1-9.7]). CONCLUSIONS: In a mostly HIV-noninfected population, a complete course of TPT conferred long-term protection against tuberculosis.
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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 | 0.006 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".