Epidemiologic Modeling to Evaluate Prevention of Mother???Infant HIV Transmission in Ontario
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of the Ontario HIV screening program to reduce mother-infant HIV transmission, this study estimated the proportion of preventable transmissions that were prevented. METHODS: Using an iterative spreadsheet model, incidences of HIV infection, AIDS, and AIDS mortality in Ontario women were estimated by exposure category. The number of HIV-infected infants born to HIV-infected mothers was then estimated from conception and abortion rates of HIV-infected women of childbearing age and surveillance data. Finally, the proportion of HIV-infected mothers who received antiretroviral prophylaxis (ARP) was assessed. RESULTS: HIV prevalence in 2001 among women of childbearing age was 1.05 per 1000. From 1984-2001, 764 infants were born to HIV-infected mothers and 180 were infected. From mid-1994-2001, 214 (39%) of the estimated 544 HIV-infected mothers were diagnosed; almost all received ARP. Of 118 preventable infections among infants born in this period, 39 (33%) were prevented. In 2001, only 46% of preventable infections were prevented and 11 preventable transmissions occurred. CONCLUSIONS: HIV prevalence among women in Ontario increased >4-fold from 1990 to 2001. Fewer than half of HIV-infected mothers received ARP and preventable HIV infections continue to occur. Measures to further increase uptake of prenatal HIV screening must be instituted.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".