Filling the gaps in the Peruvian care continuum for HIV-infected pregnant mothers: a case–control study in metropolitan Lima-Callao, Peru
Bibliographic record
Abstract
Mother-to-child transmission of HIV (MTCT) accounts for a significant proportion of new HIV infections in Peru. The purpose of this case-control study was to examine maternal and infant factors associated with MTCT in Peru from 2015 to 2016. For each biologically confirmed case infant, we randomly selected four birth year- and birth hospital-matched controls from five hospitals in Lima-Callao. Maternal and infant information were gathered from medical records. Simple conditional logistic regression was utilized to examine possible maternal and infant characteristics associated with MTCT. The rate of MTCT was 6.9% in 2015 and 2.7% in 2016. A total of 63 matched controls were identified for 18 cases. Protective factors included higher number of prenatal visits (odds ratio [OR]: 0.72; 95% confidence interval [CI]: 0.55-0.94, p = 0.012) and having more children (OR: 0.10, 95% CI: 0.01-0.79, p = 0.029). Risk factors included later maternal diagnosis (OR: 1.19; 95% CI: 1.06-1.34; p = 0.001) and greater viral load at the time of maternal diagnosis (OR: 1.05; 95% CI: 1.01-1.10; p = 0.022). Our study highlights the importance of targeting early and continued prenatal care as specific areas to target to prevent gaps in the HIV treatment cascade for pregnant HIV-infected women. These strategies can ensure early screening and initiation of antiretroviral therapy to reduce MTCT rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".