Preventive use of a hepatoprotectant against anti‐tuberculosis drug‐induced liver injury: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND AND AIM: Hepatoprotectants are routinely prescribed in China to prevent anti-tuberculosis drug-induced liver injury (ATLI). However, their biological mechanisms have not yet been clearly demonstrated. This study aims to evaluate the preventive effects of Silybum marianum against drug-induced liver injury among tuberculosis patients and to provide clinical guidelines for tuberculosis management in China. METHODS: A randomized controlled trial was performed in Jiangsu, China. Tuberculosis patients were randomly allocated to the experimental group (anti-tuberculosis therapy plus S. marianum capsule) or the control group (anti-tuberculosis therapy plus vitamin C tablet). The primary outcomes were the occurrence of probable and possible ATLI, the peak aspartate aminotransferase/alanine aminotransferase ratio and the maximum altered alkaline phosphatase or gamma-glutamyl transferase. RESULTS: The final analysis comprised 183 cases in the experiment group and 187 cases in the control group. The risk of developing probable ATLI was not significantly different between the two groups. During the follow-up period, 43.72% of cases in the experiment group and 35.83% of cases in the control group were determined to have possible ATLI (relative risk = 1.23, 95% confidence interval: 0.94-1.54). When using a more strict definition of possible ATLI, the adjusted relative risk (95% confidence interval) was 1.76 (1.14-2.56). The risks of adverse drug reactions, prolonged treatment length, taking second-line tuberculosis drugs, and the clearance of tuberculosis bacteria were similar between the two groups. CONCLUSIONS: No significant preventive effect of silymarin was found for either lowering the risk of liver injury or boosting the positive outcomes. Worse, we even found a potential risk of liver damage caused by the hepatoprotectant.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".