Estimating the prevalence and correlates of pain among people living with HIV who use unregulated drugs in a Canadian setting
Bibliographic record
Abstract
Although prevalent among people living with human immunodeficiency virus (HIV) (PLWH) and people who use unregulated drugs (PWUD), pain and its possible links to substance use patterns and engagement in HIV treatment remains poorly characterized. We sought to evaluate the prevalence and correlates of pain among a cohort of PLWH who use un-regulated drugs. Between December 2011 and November 2018, 709 participants were recruited, and data were analyzed using generalized linear mixed-effects (GLMM). At baseline, 374 (53 percent) individuals reported moderate-to-extreme pain in the previous 6 months. In a multivariable model, pain was significantly associated with nonmedical prescrip-tion-opioid use (adjusted odds ratio (AOR) = 1.63, 95 percent confidence interval (CI): 1.30-2.05), nonfatal overdose (AOR = 1.46, 95 percent CI: 1.11-1.93), self-managing pain (AOR = 2.25, 95 percent CI: 1.94-2.61), requesting pain medication in the previous 6 months (AOR = 2.01, 95 percent CI: 1.69-2.38), and ever being diagnosed with a mental illness (AOR = 1.47, 95 percent CI: 1.11-1.94). Establishing accessible pain management interventions that address the complex intersection of pain, drug use, and HIV-infection has potential to improve quality of life outcomes among this population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".