Tuberculous drug-induced liver injury and treatment re-challenge in Human Immunodeficiency Virus co-infection
Bibliographic record
Abstract
BACKGROUND: Tuberculosis drug-induced liver injury (TB-DILI) is the most common adverse event necessitating therapy interruption. The optimal re-challenge strategy for antituberculous therapy (ATT) remains unclear, especially in human immunodeficiency virus (HIV) co-infected individuals in high-prevalence settings such as South Africa. OBJECTIVE: To determine the incidence of and risk factors for the recurrence of TB-DILI with different ATT re-challenge strategies. MATERIALS AND METHODS: We conducted a retrospective chart review of patients managed for TB-DILI from 2005 to 2013 at King Edward VIII Hospital in Durban, South Africa. Relevant clinical and laboratory data at the presentation of TB-DILI, time to recovery of liver function, method of ATT re-challenge and outcome of re-challenge were documented. RESULTS: 1016 charts were reviewed, and 53 individuals with TB-DILI (48 HIV-co-infected) were identified. Following discontinuation of ATT, the median time to alanine aminotransferase normalization was 28 days (interquartile range 13-43). Forty-two subjects were re-challenged (30 regimen re-challenges and 12 step-wise re-challenges). 5 (12%) cases of recurrent TB-DILI were noted. Recurrences were not associated with the method of re-challenge. CONCLUSION: Based on the data available, it appears that full ATT can be safely restarted in the majority of subjects with a recurrence of DILI occurring in about 12% of subjects. The method of re-challenge did not appear to impact on the risk of recurrence. Ideally, a prospective randomized trial is needed to determine the best method of re-challenge.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".