Syphilis screening in pregnancy: no time for complacency
Bibliographic record
Abstract
PURPOSE OF REVIEW: Congenital syphilis continues to result in devastating adverse pregnancy and infant outcomes globally, with significant rises noted in recent years in high-income countries (HIC). Prenatal screening and prompt treatment for syphilis in pregnant persons are important in contributing to healthy pregnancy outcomes, particularly in equity-denied populations. However, the implementation of these recommendations remains challenging, even in HIC. RECENT FINDINGS: Although antenatal screening guidelines for syphilis universally recommend screening in pregnancy, the implementation of these recommendations has been challenging. In HIC, individuals grappling with poverty, unstable housing, addictions, and mental health concerns often encounter significant barriers to accessing essential healthcare services. Innovative approaches, such as the use of rapid/point-of-care tests, opportunistic screening, and community-based or outreach testing, are essential to reach key equity-denied populations. It is crucial to include members of key populations and community-based organizations in the design of interventions to effectively reach these populations. SUMMARY: Given the resurgence of congenital syphilis in some regions, especially in HIC, we must address this preventable cause of maternal and fetal morbidity and mortality effectively. Collaboration between all levels of government and health services and the inclusion of key equity-denied populations is crucial.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".