Causal Considerations on Alcohol and HIV/AIDS -- A Systematic Review
Bibliographic record
Abstract
AIM: The study aimed to explore the possible causal nature of the association between alcohol consumption and HIV/AIDS. METHODS: A review based on meta-analyses and reviews was conducted according to standard epidemiological criteria to distinguish causality from association, examining (i) the potential impact of alcohol on the incidence of HIV and (ii) alcohol's impact on worsening the disease course. RESULTS: In terms of incidence of HIV, although we found a consistent and strong association with consumption, there was not enough evidence for a causal connection. In particular, it is not clear whether personality traits such as sensation seeking or sexual compulsivity and psychiatric disorders such as antisocial personality disorder impact both alcohol consumption and risky sex, subsequently creating an association between both behaviors. In terms of worsening the disease course of HIV/AIDS, we found enough evidence for a causal impact of alcohol. Alcohol affects the immune system, thus contributing to a worsened course of HIV/AIDS. In addition, alcohol negatively impacts on behaviors that include support seeking and medication adherence. CONCLUSIONS: A randomized controlled clinical trial targeted toward at-risk HIV-negative individuals who live in areas with high HIV prevalence is suggested to test the effects of proven effective alcohol interventions on HIV incidence.
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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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".