Challenges and opportunities of universal culture-based Group B streptococcus screening in Hong Kong -- a retrospective cross-sectional study
Bibliographic record
Abstract
Objective: To evaluate gaps in universal Group B Streptococcus (GBS) screening and intrapartum antibiotic prophylaxis (IAP) in Hong Kong after its 2012 implementation, which reduced early-onset GBS disease (EOGBSD) incidence from 1.03 to 0.26 per 1000 live births. Design: Retrospective cross-sectional study. Setting: Eight Hospital Authority obstetric units and thirty-one Department of Health Maternal Child Health Centers (MCHCs), Hong Kong. Population: EOGBSD cases (2012–2022) and their mothers. Methods: Cases were electronically identified; maternal and neonatal records were reviewed for screening adherence, IAP administration, and delivery timing. Main Outcome Measures: EOGBSD incidence, screening gaps (missed tests, delayed results, prolonged screening-to-delivery intervals), and IAP compliance. Results: Among 72 EOGBSD cases, 53 eligible mothers were analyzed: 3 missed screening, 8 lacked results at delivery, and 7 delivered >5 weeks post-screening. Of 17 preterm deliveries, 41% (n=7) received no IAP due to precipitous labor (n=3), prelabor cesarean (n=3), or birth before arrival (n=1). Six neonatal deaths occurred. Conclusions: Despite successful EOGBSD reduction, critical gaps persist, including missed screenings, results unavailable at delivery, and deliveries beyond the 5-week screening validity window. Revising local protocols to address prolonged screening-to-delivery intervals and standardizing management for mothers with unknown GBS status could further reduce EOGBSD. Increased awareness and optimized workflows for preterm deliveries are needed. Keywords: Streptococcus agalactiae ; Pregnancy; Antibiotic prophylaxis; Prenatal screening; Neonatal sepsis
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".