Opioid prescriptions are associated with hepatic encephalopathy in a national cohort of patients with compensated cirrhosis
Bibliographic record
Abstract
BACKGROUND: Opioids are often prescribed for pain in cirrhosis and may increase the risk of hepatic encephalopathy (HE). AIM: To assess the association between opioids and HE in patients with well-compensated cirrhosis. METHODS: We used the IQVIA PharMetrics (Durham, NC) database to identify patients aged 18-64 years with cirrhosis. We excluded patients with any decompensation event from 1 year before cirrhosis diagnosis to 6 months after cirrhosis diagnosis. Over the 6 months after cirrhosis diagnosis, we determined the duration of continuous opioid use and classified use into short term (1-89 days) and chronic (90-180 days). We assessed whether patients developed HE over the subsequent year (ie 6-18 months after cirrhosis diagnosis). We used a landmark analysis and performed multivariable Cox proportional hazards regression to assess associations between opioid use and HE, adjusting for relevant confounders. RESULTS: The cohort included 6451 patients with compensated cirrhosis, of whom 23.3% and 4.7% had short-term and chronic opioid prescriptions respectively. Over the subsequent year, HE occurred in 6.3% patients with chronic opioid prescriptions, 5.0% with short-term opioid prescriptions and 3.3% with no opioid prescriptions. In the multivariable model, an increased risk of HE was observed with short-term (adjusted hazard ratio, HR 1.44, 95% CI 1.07-1.94) and chronic opioid prescriptions (adjusted HR 1.83, 95% CI 1.07-3.12) compared to no opioid prescriptions. CONCLUSION: In this national cohort of privately insured patients with cirrhosis, opioid prescriptions were associated with the risk of incident HE. Opioid use should be minimised in those with cirrhosis and, when required, limited to short duration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".