<scp>HIV</scp> Infection in Pregnant Women: A 2020 Update
Bibliographic record
Abstract
Acquired immunodeficiency syndrome (AIDS) was first described in 1981, and continues to be one of the worst global health pandemics in recorded history. Concerted international efforts have helped to increase awareness of human immunodeficiency (HIV) status, improve access to treatment and continuation of therapy to achieve viral suppression with a goal of ending the AIDS epidemic by 2030. The clinical outcomes for patients living with HIV on combined antiretroviral therapy are considerably improved with prolonged life expectancy and superior quality of life. Further, perinatal transmission rates have dramatically decreased with elimination of mother to child transmission of HIV in a growing number of countries worldwide. However, there have been significant reductions in the pace of progress in treatment expansion for pregnant women with failure to meet global targets in 2018. In this review, we will highlight recent advances and challenges ahead in 2020 for three areas of perinatal care for women with HIV in developed countries: (a) pregnancy planning considerations, (b) impact of antiviral medications on perinatal outcomes, and (c) infant feeding practices. The promise of a HIV-free generation is on the horizon and continued international efforts in preventing perinatal transmission are an important component of this achievement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".