The effect of human immunodeficiency virus infection on adverse events during treatment of drug-resistant tuberculosis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Adverse events (AEs) during drug-resistant tuberculosis (DR-TB) treatment, especially with human immunodeficiency virus (HIV) co-infection, remains a major threat to poor DR-TB treatment adherence and outcomes. This meta-analysis aims to investigate the effect of HIV infection on the development of AEs during DR-TB treatment. METHODS: Eligible studies evaluating the association between HIV seropositivity and risks of AE occurrence in DR-TB patients were included in this systematic review. Interventional and observational studies were assessed for risk of bias using the Risk of Bias in Nonrandomized Studies of Intervention and Newcastle-Ottawa Scale tool, respectively. Random-effects meta-analysis was performed to estimate the pooled risk ratio (RR) along with their 95% confidence intervals (CIs). RESULTS: A total of 37 studies involving 8657 patients were included in this systematic review. We discovered that HIV infection independently increased the risk of developing AEs in DR-TB patients by 12% (RR 1.12 [95% CI: 1.02-1.22]; I2 = 0%, p = 0.75). In particular, the risks were more accentuated in the development of hearing loss (RR 1.44 [95% CI: 1.18-1.75]; I2 = 60%), nephrotoxicity (RR 2.45 [95% CI: 1.20-4.98], I2 = 0%), and depression (RR 3.53 [95% CI: 1.38-9.03]; I2 = 0%). Although our findings indicated that the augmented risk was primarily driven by antiretroviral drug usage rather than HIV-related immunosuppression, further studies investigating their independent effects are required to confirm our findings. CONCLUSION: HIV co-infection independently increased the risk of developing AEs during DR-TB treatment. Increased pharmacovigilance through routine assessments of audiological, renal, and mental functions are strongly encouraged to enable prompt diagnosis and treatment in patients experiencing AEs during concomitant DR-TB and HIV treatment.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".