Clinical impact of recreational drug use among people living with HIV in southern Taiwan
Bibliographic record
Abstract
BACKGROUND: It is unclear about the impact of recreational drug use on the adherence, drug-drug interaction and the occurrence of sexual transmitted diseases (STDs) among people living with HIV. MATERIAL AND METHODS: A retrospective study was conducted between Dec 2016, and July 2018 to assess the clinical impact of recreational drug consumption in people living with HIV with antiretroviral therapy. We collected data of the demographics, recreational drug use, laboratory results and STDs diagnoses. Potential drug-drug interactions were checked with reference databases. The association between recreational drug use and STDs, HIV viral load suppression and drug interactions were evaluated. RESULTS: A total of 462 participants were enrolled, included 384 recreational drug users and 78 non-recreational drug users. Younger age (adjusted odds ratio [aOR], 0.94; 95% CI: 0.91-0.98; p = 0.001), longer HIV infection period (aOR, 1.11; 95% CI: 1.03-1.20; p = 0.009) and poor antiretroviral drug adherence (1-2 pills missing per month: aOR, 6.82; 95% CI: 3.50-13.27; p < 0.001; >2 pills missing per month: aOR, 3.50; 95% CI: 1.28-9.61; p = 0.015) were factors associated with recreational drug use. Methamphetamine and nitrites were two most common recreational drugs. Recreational drug use was significantly associated with STDs in one-year follow-up period (aOR, 2.43; 95% CI: 1.11-5.32; p = 0.027) but was not significantly associated with unsuppressed viral load, though a trend was observed (OR, 2.23; 95% CI: 0.92-5.37; p = 0.074). Potential interactions with recreational drugs included 33.1% antiretroviral drugs and 31.3% medications for comorbidities. CONCLUSION: Recreational drug was associated with STDs. A great proportion of the patients consuming recreational drugs had potential interactions with antiretroviral drugs and medications for comorbidities. The association of recreational drug use and unsuppressed viral load warrants further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".