Characterising the association between positive hepatitis C virus antibody and pain among people who inject drugs
Bibliographic record
Abstract
INTRODUCTION AND AIMS: People who inject drugs (PWID) are a key group within the hepatitis C virus (HCV) pandemic. Chronic pain is a common condition among PWID as these individuals are often exposed to soft tissue infections due to injections and violence. This study aims to characterise the relationship between HCV exposure and pain among PWID. DESIGN AND METHODS: Data were derived from three prospective cohorts of PWID in Vancouver, Canada, between December 2011 and November 2016. The primary outcome was pain severity, which was defined based on the Euroqol EQ-5D-3L pain subscale. A bivariable and multivariable ordinal generalised estimating equations model was used to quantify the association between HCV exposure and pain among participants. RESULTS: One thousand and twelve of 2038 participants (50%) reported moderate/extreme pain at baseline. In total, 1473 (72%) participants were HCV-antibody positive. In unadjusted analyses, HCV exposure was positively associated with increased pain [odds ratio (OR) = 1.47; 95% confidence interval (CI): 1.20-1.81]. However, once adjusted for known confounders in multivariable analyses, HCV exposure did not remain significantly associated with increased pain (adjusted OR = 1.00; 95%CI: 0.78-1.28). DISCUSSION AND CONCLUSIONS: In this sample of PWID, HCV exposure was not significantly associated with pain once other factors were considered. These various factors may explain the elevated risk of pain among PWID and should be addressed in future initiatives when managing pain among PWID with HCV exposure. Future studies should also examine whether pain changes with changes in HCV status (i.e. active vs. cleared infection).
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".