Use of Isoniazid Preventive Therapy for Tuberculosis Prophylaxis Among People Living With HIV/AIDS
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is the leading preventable cause of death in persons living with HIV (PLHIV), accounting for over a quarter of all HIV-associated deaths in 2012. Isoniazid preventive therapy (IPT) has the potential to decrease TB-related cases and deaths in PLHIV; however, implementation of this has been slow in many high HIV- and TB-burden settings. METHODOLOGY: We performed an assessment of the evidence for the use of IPT in adults living with HIV based on a review of the literature published from 1995 to 2013. Eligible articles included data on mortality, morbidity, or retention in care related to the provision of IPT to adults with HIV in low- or middle-income countries. Cost-effectiveness information was also abstracted. RESULTS: We identified 41 articles involving over 45,000 PLHIV. While there was little evidence to demonstrate that IPT reduced mortality in PLHIV, there was substantial evidence that IPT reduced TB incidence. While these findings were consistent irrespective of CD4 or antiretroviral therapy status, studies frequently demonstrated a greater benefit among patients with a positive TB skin test (TST). Duration of effectiveness and benefits of prolonged therapy varied across settings. CONCLUSIONS: This analysis supports World Health Organization recommendations for the provision of IPT to PLHIV to reduce TB-associated morbidity and serves to highlight the need to strengthen IPT implementation. While there appears to be a greater benefit of IPT among PLHIV who are TST positive, IPT should be provided to all PLHIV without presumptive TB when TST is not available.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".