Strategies to Prevent Early and Late-Onset Group B Streptococcal Infection via Interventions in Pregnancy
Bibliographic record
Abstract
is a Gram-positive bacterium that typically colonizes 10-30% of pregnant women, causing chorioamnionitis, preterm birth, and stillbirth, as well as neonatal sepsis and meningitis with early-onset disease (EOD) or late-onset disease (LOD) due to ascending infection or transmission during delivery. While there are some differences between EOD and LOD in terms of route of transmission, risk factors, and serotypes, the only preventive approach currently is maternal intrapartum antibiotic prophylaxis (IAP) which will not be able to fully address the burden of the disease since this has no impact on LOD. Probiotics and immunization in pregnancy may be more effective than IAP for both EOD and LOD. There is mixed evidence of probiotic effects on the prevention of GBS colonization, and the data from completed and ongoing clinical trials investigating different GBS vaccines are promising. Current vaccine candidates target bacterial proteins or the polysaccharide capsule and include trivalent, tetravalent, and hexavalent protein-polysaccharide conjugate vaccines. Some challenges in developing novel GBS vaccines include the lack of a correlate of protection, the potential for serotype switching, a need to understand interactions with other vaccines, and optimal timing of administration in pregnancy to maximize protection for both term and preterm infants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".