The effects of paracetamol (acetaminophen) on hepatic tests in patients who chronically abuse alcohol – a randomized study
Bibliographic record
Abstract
BACKGROUND: Retrospective accounts suggest that therapeutic doses of paracetamol can produce severe hepatic injury in patients with putative high-risk conditions, including alcoholism and infectious hepatitis. Metabolism of paracetamol to its hepatotoxic metabolite is enhanced in patients who abuse alcohol, who also have compromised liver defences from depressed hepatic glutathione. AIM: To determine the effect of paracetamol on serum liver tests of newly abstinent subjects who abuse alcohol, including subjects with hepatitis C infection. METHODS: A randomized, double-blind, placebo-controlled study. Adult alcohol abusers with a current drinking episode longer than 7 days received either placebo or paracetamol 4 g/day for 5 days. RESULTS: Of 142 subjects enrolled, 74 received paracetamol and 68 received placebo. Mean ALT activity during treatment increased from 48 to 62 IU/L in the paracetamol group and from 47 to 49 IU/L in the placebo group. Maximum ALT was 238 and 249 IU/L in the paracetamol and control groups respectively. The INR remained unchanged and serum bilirubin decreased in both groups. Subgroup analyses for subjects with alcoholic hepatitis, hepatitis C virus antibody and other subgroups showed no statistical difference between groups. CONCLUSION: Administration of paracetamol 4 g/day appears safe in newly abstinent patients who abuse alcohol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".