Prior history of testing for syphilis, hepatitis B and hepatitis C among a population-based cohort of HIV-positive individuals and their HIV-negative controls
Bibliographic record
Abstract
Understanding patterns of serological testing for hepatitis B & C, and syphilis among HIV-positive individuals, prior to HIV diagnosis, can inform HIV diagnosis, engagement and prevention strategies. This was a population-based, retrospective analysis of prior serological testing among HIV-positive individuals in Manitoba, Canada. HIV cases were age-, sex- and region-matched to HIV-negative controls at a 1:5 ratio. Conditional logistic regression was used to examine previous serological tests and HIV status. Odds ratios (ORs) and their 95% confidence intervals (95% CI) were reported. A total of 193 cases and 965 controls were included. In the 5 years prior to diagnosis, 50% of cases had at least one test, compared to 26% of controls. Compared to those who did not have serological testing in the 5 years prior to HIV infection, those who had one serological test were at twice the odds of being HIV positive (OR: 1.9, 95% CI: 1.2-2.9), while those with 2 or more tests were at even higher odds (OR: 5.5, 95%CI: 3.7-8.4). HIV cases had higher serological testing rates. Interactions between public health and other healthcare providers should be strengthened.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".