Effect of Injecting Drug User on the Risk of Human Immunodeficiency Virus/ Acquired Immunodeficiency Syndrome: A Meta-Analysis
Bibliographic record
Abstract
Background: The problem of narcotics abuse is drug users through injecting needles. Injecting drug users are any person who uses narcotics, psychotropics and addictive substances by injection. In addition, another understanding states that injecting drug users (IDU) or Injection Drug Users (IDUs) are users of narcotics/drugs with injecting media. Injecting drug use (IDU) is one of the main causes of HIV infection due to sharing of contaminated injection equipment.Subjects and Method: The meta-analysis was carried out using the PRISMA flowchart and the PICO model. Population = adolescents and adults. Intervention= IDU/Injection Drug User. Comparison= Not an IDU/Injection Drug User. Outcome= HIV/AIDS events. The articles used in this study were obtained from several databases including PubMed, Google Scholar and Scopus. These articles were collected over 3 months. The keywords to search for articles are as follows “IDU (Injection Drug User)” AND “life style“ ”HIV/ AIDS ”. There were 15 studies, 9 cross-sectional and 6 cohort studies published in 2012-2022 that met the inclusion criteria. Analysis was performed with Revman 5.3.Results: 15 articles with a study design of 9 cross-sectional and 6 cohort studies from Canada, China, Ukraine, Virginia, Nepal, Cambodia, Scotland, Boston and Africa. Studies show that IDUs (Injecting Drug Users) have a 2.17 times risk of developing HIV/AIDS compared to non-IDUs for HIV/AIDS, and these results are statistically significant (aOR= 2.71; 95% CI= 1.22 to 6.02; p= 0.010).Conclusion: IDU (Injection Drug User) increases the incidence of HIV (Human Immunodeficiency Virus) / AIDS (Acquired Immunodeficiency Syndrome). Keywords: IDU, lifestyle, HIV/AIDS, Drugs, Meta-Analysis Correspondence:Lusiana Dewi Saputri. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: dewislusiana16@gmail.com. Mobile: +6282328370049.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.053 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".