Social vulnerability in persons with chronic hepatitis C virus infection is associated with a higher risk of prescription opioid use
Bibliographic record
Abstract
Prescription opioid use (POU) is often a precursor to opioid use disorder (OUD) and subsequent consequences. Persons with chronic hepatitis C virus infection (CHC) may be at a higher risk of POU due to a higher comorbidity burden and social vulnerability factors. We sought to determine the burden of POU and associated risk factors among persons with CHC in the context of social vulnerability. We identified CHC persons and propensity-score matched HCV- controls in the electronically retrieved Cohort of HCV-Infected Veterans and determined the frequency of acute, episodic long-term and chronic long-term POU and the prevalence of social vulnerability factors among persons with POU. We used logistic regression analysis to determine factors associated with POU. Among 160,856 CHC and 160,856 propensity-score matched HCV-controls, acute POU was recorded in 38.4% and 38.0% (P = 0.01) respectively. Episodic long-term POU was recorded in 3.9% in each group (P = 0.5), while chronic long-term POU was recorded in 28.4% and 19.2% (P < 0.0001). CHC was associated with a higher risk of chronic long-term POU (OR 1.66, 95%CI 1.63, 1.69), but not with acute or episodic long-term POU. Black race, female sex and homelessness were associated with a higher risk of chronic long-term POU. Presence of ≥ 1 factor was associated with a higher risk of all POU patterns. Persons with CHC have more social vulnerability factors and a higher risk of chronic long-term POU. Presence of ≥ 1 social vulnerability factor is associated with a higher risk of POU. Downstream consequences of POU need further study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".