Examination of Care Milestones for Preventing Congenital Syphilis Transmission Among Syphilis-Infected Pregnant Women in Alberta, Canada: 2017–2019
Bibliographic record
Abstract
BACKGROUND: An infectious syphilis outbreak in Alberta has resulted in increased congenital syphilis (CS) cases. To shed light on potential risk factors, we used administrative data sets to examine care milestones for the prevention of CS among pregnant women diagnosed with syphilis, as well as correlates of women giving birth to infants with CS. METHODS: Provincial administrative databases were used to identify and describe pregnant women diagnosed with any stage of infectious or noninfectious syphilis who gave birth in Alberta between January 1, 2017, and December 31, 2019. Data on prenatal care, syphilis screening, and syphilis medication dispensation were used to evaluate the care milestones. Clinical care and maternal demographics were assessed using logistic and linear regression analyses to determine correlates for missed care milestones or a newborn outcome of CS. RESULTS: Of 182 syphilis-infected pregnant women, 63 (34.6%) delivered a newborn with CS. Overall, in the first trimester, 136 (75.1%) women had a health care visit, 72 (39.6%) had a prenatal care visit, 71 (39.0%) were screened for syphilis, and 44 (24.2%) were treated. Gestational time to treatment initiation (adjusted odds ratio, 1.04; 95% confidence interval, 1.02-1.06) and older maternal age at diagnosis (adjusted odds ratio: 1.28, 95% confidence interval, 1.08-1.50) were independently associated with CS outcomes. No variables were found to be independently associated with a health care visit, prenatal screening, or initiation of treatment. CONCLUSIONS: Although nearly two-thirds of CS cases were prevented, there remained missed opportunities in the prevention of CS. Early treatment, which relies on timely access to prenatal care and screening, was the most important for the prevention 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".