Clinical and regulatory development strategies for GBS vaccines intended for maternal immunisation in low- and middle-income countries
Bibliographic record
Abstract
Group B Streptococcus (GBS) is a leading cause of infant mortality, particularly in low- and middle-income countries (LMICs). Several maternal GBS vaccine candidates, aimed at protecting infants, are progressing through clinical trials. The World Health Organisation (WHO) aims to ensure equitable access to safe, effective, and affordable vaccines of assured quality in LMICs, by facilitating regulatory pathways. An alternate approval pathway, based on safety and an immunological endpoint thought to predict clinical benefit (commonly referred to as serological threshold of risk reduction [SToRR]), is being considered for GBS maternal vaccines. Since this approach is new to many LMICs regulators and policymakers, WHO organized consultative meetings at national, regional, and global levels to discuss the feasibility and potential challenges of approving a GBS vaccine based on safety and immunogenicity data alone. These consultations focused on evidence supporting SToRR, their use as endpoints to infer protection, and post-licensure requirements. The aim of the consultations was to reduce the delay between vaccine development, licensure, policy recommendations and use in high-burden LMICs.
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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 | 0.103 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".