Determinants of Treatment Access in a Population‐based Cohort of HIV‐positive Men and Women Living in Argentina
Bibliographic record
Abstract
OBJECTIVE: To report emerging data on the use of highly active antiretroviral therapy (HAART) in Argentina by assessing patterns of HAART access and late vs early treatment initiation in a population-based cohort of adults infected with HIV type-1. DESIGN: The Prospective Study on the Use and Monitoring of Antiretroviral Therapy (PUMA) is a study of 883 HIV-positive individuals enrolled in the Argentinean drug treatment program. Individuals were 16 years of age and older and were recruited from 10 clinics across Argentina. METHODS: Sociodemographic and clinical characteristics were examined using contingency tables (Pearson chi-square test and Fisher exact test) for categoric variables and Wilcoxon rank-sum test for continuous variables. To analyze time to initiation of HAART we used Kaplan-Meier methods and Cox regression. RESULTS: Patients who initiated HAART were more likely to be older, have an AIDS-defining illness, be an injection drug user (IDU), have a lower median CD4 cell count, have a higher median viral load, and be less likely to be men who have sex with men (MSM). In multivariate analysis, AIDS-defining illness and plasma viral load were significantly associated with time to starting therapy. Patients who received late access were more likely to be diagnosed with AIDS and have higher median plasma viral loads than those receiving early access. CONCLUSION: Our results indicate that despite free availability of treatment, monitoring, and care in Argentina, a significant proportion of men and women are accessing HAART late in the course of HIV disease. Further characterization of the HIV-positive population will allow for a more comprehensive evaluation of the impact of HAART within the Argentinean drug treatment program.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".