Association of BMI with mortality in drug-induced liver injury
Bibliographic record
Abstract
BACKGROUND: To clarify the associations between BMI and the incidences of all-cause death or liver-related death (LRD)/liver transplantation (LT) in drug-induced liver injury (DILI). METHODS: DILI patients from three hospitals were retrospectively retrieved and follow-up from 2009 to 2021. They were categorized into underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI of 18.5-23.9 kg/m 2 ), overweight (BMI of 24-27.9 kg/m 2 ) and obese (BMI ≥ 28 kg/m 2 ) groups. Cox regression models were conducted to reveal the effect of BMI on all-cause death or LRD/LT. RESULTS: A total of 1469 eligible DILI patients were included: underweight 73 (4.97%), normal weight 811 (55.21%), overweight 473 (32.20%) and obese 112 (7.62%). Eighty-nine patients (6.06%) had all-cause death, of which 66 patients (4.49%) had LRD/LT. The median age was 52 years old, and females were 1039 (70.73%). The associations between BMI and all-cause mortality ( nonlinear test P < 0.01) or liver-related mortality/LT ( nonlinear test P = 0.01) were J-shaped. Multivariate Cox regression analysis showed that underweight (HR: 3.02, 95% CI: 1.51-6.02) was significantly associated with all-cause mortality after adjusting for age and sex. Furthermore, obese males were significantly associated with liver-related mortality/LT (HR: 3.49, 95% CI: 1.13-10.72) after additional adjustment for serological indices and comorbidities. CONCLUSION: Association between BMI and mortality is a J-shape. The overall mortality was significantly higher in underweight and obese group. Male obesity is independently associated with LRD/LT. These findings indicate that DILI patients with extreme BMI would have a high risk of dismal outcomes, which warrants extra medical care.
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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 | 0.001 |
| 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".