Time to Testing and Accessing Care among a Population of Newly Diagnosed Patients with HIV with a High Proportion of Canadian Aboriginals, 1998–2003
Bibliographic record
Abstract
Early HIV diagnosis and treatment are important for decreasing HIV transmission and morbidity. By using initial CD4 counts and time to first viral load test, we examined the stage of disease at the time of diagnosis and the time to accessing medical care after diagnosis, respectively. Initial CD4 count, first HIV viral load test, demographics and exposure risks were obtained for all newly diagnosed HIV cases in Northern Alberta from 1998-2003. Time to accessing care was determined as the time between diagnosis and the first viral load test. Correlates were determined using simple descriptive statistics and survival analysis methods. Of 526 HIV cases, median age was 36 years (interquartile range [IQR]: 31-43), 69% were males and 41% were Aboriginal. At diagnosis, 28% of the population had CD4 counts less than 200 cells=mm3. After diagnosis, 92.2% accessed care and median time to care for the entire population was 29 days. In multivariate analysis, age at diagnosis less than 45 years was independently associated with longer median time to care (versus age 45 years or more; adjusted hazard ratio [AHR]: 0.69; 95% confidence interval [CI] 0.55-0.88), while Aboriginal ethnicity (versus Caucasian; AHR: 0.82; 95% CI 0.68-1.01), and nonmetropolitan residence (versus metropolitan; AHR: 0.81; 95% CI 0.65-1.00) were marginally significant correlates for longer times to care. Although more than one quarter of cases were diagnosed at relatively advanced stages of infection, the majority of new HIV cases in Northern Alberta accessed care within 2 months of diagnosis. We need to explore new strategies to facilitate and promote earlier access to testing among individuals at risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".