Uptake of Combination Antiretroviral Therapy and HIV Disease Progression According to Geographical Origin in Seroconverters in Europe, Canada, and Australia
Bibliographic record
Abstract
BACKGROUND: We examined differences by geographical origin (GO) in time from HIV seroconversion (SC) to AIDS, death, and initiation of antiretroviral therapy (cART). METHODS: Data from HIV seroconverter cohorts in Europe, Australia and Canada (CASCADE) was used; GO was classified as: western countries (WE), North Africa and Middle East (NAME), sub-Saharan Africa (SSA), Latin America (LA), and Asia (ASIA). Differences by GO were assessed using Cox models. Administrative censoring date was 30 June 2008. RESULTS: Of 16 941 seroconverters, 15 548 were from WE, 158 NAME, 762 SSA, 349 LA, and 124 ASIA. We found no differences by GO in risks of AIDS (P = .99) and death (P = .12), although seroconverters from NAME (adjusted hazard ratio [aHR]: 0.57; 95% CI: 0.33-.94) and SSA (aHR: 0.74; 95% CI: 0.50-1.10) appeared to have lower mortality than WE. Chances of initiating cART differed by GO (P < .001): seroconverters from SSA were more likely to initiate cART than WE (aHR: 1.48; 95% CI: 1.26-1.74), but not after adjustment for CD4 at SC (aHR: 1.11; 95% CI: 0.88-1.40). CONCLUSIONS: In settings with universal access to healthcare, GO does not play a major role in HIV disease progression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".