Mapping the incidence of drug‐induced liver injury: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Drug-induced liver injury (DILI) is an increasing etiology of liver dysfunction, with various incidence worldwide. To better understand the disease burden and establish appropriate preventive and treatment strategies, a systematic review and meta-analysis was conducted. METHODS: PubMed, EMBASE, Web of Science, and Cochrane Library were searched for studies on the incidence of DILI published up to June 1, 2022. According to the predefined criteria, only population-based studies were included. Incidence was presented as cases per 100 000 person-years with 95% confidence interval (CI) using a random-effects model. RESULTS: A total of 14 studies were included. The overall incidence of DILI was 4.94 per 100 000 person-years (95% CI 4.05-5.83). Time-based cumulative meta-analysis suggested that the incidence of DILI increased over time since 2010. The incidence varied by regions, with Asia having the highest incidence of 17.82 per 100 000 person-years (95% CI 6.26-29.38), while North America having the lowest incidence of 1.72 per 100 000 person-years (95% CI 0.48-2.95). All studies reported a higher incidence of DILI in the elderly but comparable incidences between male and female (3.42 per 100 000 person-years vs 4.64 per 100 000 person-years). CONCLUSIONS: The global incidence of DILI has been increasing since 2010, with the highest incidence in Asia. Understanding the epidemiological characteristics of DILI helps establish specific strategies to deal with this emerging health problems.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".