Rapid Testing at Labor and Delivery to Prevent Mother-To-Child Hiv Transmission in Developing Settings: Issues and Challenges
Bibliographic record
Abstract
Worldwide, approximately 2.5 million children (95% CI: 2.2-2.6) are living with HIV infection. In 2007 alone, approximately 420,000 children (95%CI: 350,000-540,000) were newly infected with HIV - a vast majority of these infections were acquired through maternal-fetal transmission. Many of these infections could have been reduced by timely diagnosis and the delivery of interventions aimed at preventing mother-to-child HIV transmission. This perspective examines the attitudes preventing women from accessing HIV testing early on during pregnancy and the issues and challenges that remain in the institutionalization of interventions to prevent mother-to-child HIV transmission at labor and delivery. Socio-cultural and economic factors prevent women from accessing testing at an opportune time during pregnancy. In addition, a lack of adequate infrastructure often prevents timely delivery of interventions to those who access testing at the last minute (i.e., during labor and delivery). In the wake of a pediatric HIV epidemic and the need for lifelong provision of antiretroviral therapy to infected children, a simple strategy for provision of round-the-clock rapid testing and counseling services in the labor rooms may be cost saving to the healthcare systems worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".