Non-Genetic Risk Factors for First-Line Anti-Tuberculosis Drug-Induced Liver Injury in Active Pulmonary Tuberculosis Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Tuberculosis is still one of the leading causes of poor health and death worldwide. Drug-induced liver injury (DILI) is an important and serious side effect of anti-tuberculosis treatment and can cause non-adherence of patients to the treatment. To reduce the possibility of patients developing DILI, the risk factors must be identified. Methods: This systematic review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Inclusion and exclusion criteria were used to screen and filter the articles that were obtained from literature searching performed through several journal databases. The extracted data were analyzed qualitatively and quantitatively. The quality of each study was also assessed using the modified Newcastle Ottawa Scale (NOS). The protocol for this systematic review has been registered with PROSPERO CRD42022384892. Results: The results showed that, of the 13 studies analyzed qualitatively, 11 studies with a total of 4,920 patients were selected for quantitative analysis. The factors analyzed and the results were female gender (OR=1.10; 95% CI=0.72-1.67; P=0.65), age over 40 years (OR=1.60; 95% CI=1.04-2.46; P=0.03), body mass index less than 18.5 kg/m2 (OR=0.96; 95% CI=0.52-1.79; P=0.9), active smoking (OR=0.71; 95% CI=0.34-1.49; P=0.36), frequent alcohol intake (OR=1.44; 95% CI=0.61-3.42; P=0.41), hepatitis B (OR=3.42; 95% CI=1.72-6.79; P<0.001]), and hepatitis C (OR=12.87; 95% CI=6.67-24.86; P<0.00001]). Conclusion: In conclusion, the evidence from this review suggests that older age, hepatitis B, and hepatitis C are significant risk factors thought to increase the incidence of DILI in active pulmonary tuberculosis patients taking first-line anti-tuberculosis regimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".