New DNA-based PCR approaches for rapid real-time detection and prevention of group B streptococcal infections in newborns and pregnant women
Bibliographic record
Abstract
Group B streptococci (GBS) are an important cause of neonatal sepsis and meningitis, and maternal infection. Although the pathogenesis of GBS infection is not well understood, several virulence factors have been identified. Two prevention strategies have been proposed: chemoprophylaxis and immunoprophylaxis. Implementation of selective intrapartum chemoprophylaxis on the basis of either screening or risk assessment has led to a substantial decrease in the morbidity and mortality of GBS disease in both mothers and infants. Penicillin remains the antibiotic of choice with no reported resistant GBS so far, whereas resistance of 10-20% of GBS to erythromycin and clindamycin has been reported in North America. Chemoprophylaxis based on screening requires optimal detection methods for GBS, which involve selective broth culture of combined vaginal and anal samples. Other conventional methods are useful for rapid identification of heavily colonised women, but are unreliable for the detection of light GBS colonisation because of poor sensitivity. GBS-specific polymerase chain reaction (PCR) assays using real-time PCR coupled with fluorescence-labelling technology offer powerful tools for sensitive and specific, yet rapid (less than 1 h), detection of GBS directly from clinical specimens at the time of delivery. The application of these assays to the current prevention strategies will simplify the prevention practice and rationalise the use of antibiotics. Immunoprophylaxis relies on the development of new vaccines against GBS, and active research is being conducted in this area.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".