Assesing Conversion Rate, Treatment Success and Mortality of Drug Induce Liver Injury from First Line Antituberculosis Regiment: A Systematic Review and Meta -Analysis
Bibliographic record
Abstract
<ns5:p> The prevalence of Drug Induce Liver Injury (DILI) in Tuberculosis (TB) patients is a critical concern, as it complicates the treatment regimen. The hepatotoxicity of first-line anti-TB drugs such as isoniazid and rifampicin can lead to significant liver damage, which in turn affects the metabolism of these medications, potentially resulting in treatment failure or increased mortality. Given the limited certainty surrounding the clinical outcomes of tuberculosis-associated drug-induced liver injury (TB-DILI), an in-depth evaluation of existing evidence is essential. This systematic review and meta-analysis aimed to assess the clinical outcomes in patients with TB-DILI. Comprehensive searches were conducted across the Scopus, Embase, and PubMed databases, adhering to inclusion criteria derived from the PICOS framework. Keywords related to "drug-induced liver injury" and "tuberculosis," as well as their synonyms, were employed in the search. The Newcastle-Ottawa Scale (NOS) was utilized to assess the risk of bias in observational studies. Data were independently extracted, and the quality of the included studies was evaluated. Relative risk (RR) and tests for heterogeneity were conducted, and results were visualized through RR estimates and forest plots. A total of five studies, encompassing 5,798 patients, were ultimately included in the analysis. This study indicates that TB DILI has no significant risk on 2 months conversion compared to TB Non – DILI patients with RR: 1.03; 95%CI: 0.97–1.03, <ns5:italic>p</ns5:italic> <0.31. However, treatment success (RR: 1.11; 95%CI: 1.05–1.18, <ns5:italic>p</ns5:italic> <0.0004) and mortality risk (RR: 2.62; 95%CI: 1.14–6.03, <ns5:italic>p</ns5:italic> <0.02) were significant in TB Non – DILI patients. </ns5:p>
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".