Integrating law, coverage, and maternal–fetal medicine workflows to eliminate congenital syphilis: A systematic review and policy synthesis
Bibliographic record
Abstract
ABSTRACT Congenital syphilis (CS) persists as a preventable cause of stillbirth and neonatal morbidity despite the availability of simple screening and curative treatment. Fragmented legal mandates, inconsistent payer coverage, and variable maternal–fetal medicine (MFM) workflows contribute to preventable transmission. This systematic review and policy synthesis integrates global evidence to define a modern framework for eliminating CS through coordinated action across law, financing, and clinical care. Comprehensive searches of biomedical databases, guidelines, and policy registers identified 2292 records; 27 studies met the inclusion criteria following Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 standards. Sources included systematic reviews, national guidelines, modeling studies, and legal documents. Data were extracted on screening schedules, treatment regimens, coverage mechanisms, and workflow integration. Quality was assessed using A Measurement Tool to Assess Systematic Reviews, version 2, Risk of Bias in Systematic Reviews, and Newcastle–Ottawa tools. Across studies, universal early testing, third-trimester and delivery rescreening, and benzathine penicillin access were consistently associated with improved outcomes. National mandates and payer reimbursement policies increased screening compliance, while electronic health record alerts and point-of-care testing shortened treatment delays. However, persistent inequities affected low-resource and minority population. Synthesized findings support a “triple-lock” model – binding legal mandates, guaranteed financial coverage, and digital workflow automation – as the pathway to sustainable elimination. The review calls for harmonized laws, procurement transparency, and integrated partner management within antenatal care. Although not registered in PROSPERO due to its cross-disciplinary scope, this review provides the most comprehensive evidence-policy bridge to date for the global eradication of CS.
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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 | 0.049 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".