Evaluation of the Electronic Health Record as a Tool for Maternal and Congenital Syphilis Surveillance
Bibliographic record
Abstract
BACKGROUND: Early entry into prenatal care and timely syphilis diagnosis and treatment of pregnant women can prevent congenital syphilis (CS). We assessed syphilis testing and treatment for pregnant women and their infants using electronic health records (EHRs). METHODS: We extracted syphilis testing results from pregnancy episodes documented in EHRs for women seeking care at the Medical University of South Carolina health system during 2021-2022. Chart reviews confirmed syphilis diagnosis, stage, and treatment. We calculated percentages of pregnant women who were (1) screened for syphilis, (2) received abnormal (i.e., reactive) test results, (3) newly diagnosed with syphilis, and (4) adequately treated for syphilis before delivery. We assessed testing and treatment outcomes of infants exposed to maternal syphilis. RESULTS: Among 12,959 pregnant women, 75% (n = 9708) had ≥1 syphilis test during pregnancy; 106 (1.1%) had ≥1 abnormal test. From chart reviews, 54 women (51%) with abnormal test results were newly diagnosed with syphilis. The remaining abnormal test results were false positives (n = 15) or previous diagnoses (n = 37). Forty-four (81%) new diagnoses were treated during pregnancy; 30 (56%) were treated >30 days before delivery. Fifty-six infants born to women with an abnormal syphilis test result were evaluated for CS, of whom 24 (43%) had abnormal test results and 18 (75%) were treated for CS. CONCLUSIONS: Although EHRs could reliably assess syphilis testing during pregnancy, chart review and consultation with public health authorities were necessary to confirm adequate treatment and CS follow-up. Triangulating EHRs with other data sources could enhance the understanding of syphilis during pregnancy and inform CS prevention efforts.
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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 | 0.034 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".