Alcohol Consumption, Progression of Disease and Other Comorbidities, and Responses to Antiretroviral Medication in People Living with HIV
Bibliographic record
Abstract
The present paper describes the possible connection between alcohol consumption and adherence to medicine used to treat human deficiency viral (HIV) infection. Highly active antiretroviral therapy (HAART) has a positive influence on longevity in patients with HIV, substantially reducing morbidity and mortality, including resource-poor settings such as South Africa. However, in a systematic comparison of HAART outcomes between low-income and high-income countries in the treatment of HIV-patients, mortality was higher in resource-poor settings. Specifically, in South Africa, patients often suffer from concomitant tuberculosis and other infections that may contribute to these results. Alcohol influences the use of medicine for opportunistic infections (e.g., pneumonia, tuberculosis), or coinfections HIV-hepatitis viruses-B (HBV) and C (HCV), cytomegalovirus, or herpes simplex virus. Furthermore, alcohol use may negatively impact on medication adherence contributing to HIV progression. The materials used provide a data-supported approach. They are based on analysis of published (2006-2011) world literature and the experience of the authors in the specified topic. Intended for use by health care professionals, these recommendations suggest approaches to the therapeutic and preventive aspects of care. Our intention was to fully characterize the quality of evidence supporting recommendations, which are reflecting benefit versus risk, and assessing strength or certainty.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".