Hepatitis C is a predictor of acute liver injury among hospitalizations for acetaminophen overdose in the United States
Bibliographic record
Abstract
UNLABELLED: Acute liver injury (ALI) following acetaminophen overdose (AO) occurs in less than 10% of cases, but that risk is increased among alcoholics and those with chronic alcoholic liver disease. We sought to assess whether coexistent hepatitis C virus (HCV) infection potentiated the hepatotoxic effects of acetaminophen. We queried the Nationwide Inpatient Sample (1998-2005), a 20% sample of U.S. hospitals, to identify admissions for AO using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Outcomes were development of ALI (ICD-9-CM: 570.0, 572.2, 573.3), in-hospital mortality, severe liver failure, and resource utilization. There were 42,781 admissions for AO in the sample, yielding a national estimate of 210,436 AO hospitalizations. HCV prevalence increased from 0.5% to 1.5% between 1998 and 2005 (P < 0.0001). The rate of ALI was 7.2%. After adjusting for confounders and excluding patients with cirrhosis, the risk of ALI increased with HCV (adjusted odds ratio [aOR] 1.80; 95% confidence interval [CI]: 1.30-2.48), nonalcoholic fatty liver disease (aOR 7.43; 95% CI: 3.30-16.7), alcoholic liver disease (aOR 6.46; 95% CI: 4.53-9.21), and malnutrition (aOR 3.84; 95% CI: 2.61-5.65). HCV was associated with greater risk of progression to severe liver failure (aOR 3.55; 95% CI: 1.88-6.70). Crude mortality was higher in patients with HCV compared to those without HCV (2.1% versus 0.9%, P = 0.01); patients with ALI had an overall mortality of 8.6%. Length of stay was longer in patients with HCV (4.0 versus 2.6 days, P < 0.0001). Admissions with coexistent HCV also incurred two-fold higher hospital charges than those that did not ($21,400 versus $11,400, P < 0.0001). CONCLUSION: Our retrospective analysis suggests that patients with HCV may be at increased risk of ALI following AO. These findings warrant further confirmation in prospective studies.
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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 | 0.001 | 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".