Clinical and regulatory development strategies for Shigella vaccines intended for children younger than 5 years in low-income and middle-income countries
Bibliographic record
Abstract
Shigellosis causes considerable public health burden, leading to excess deaths as well as acute and chronic consequences, particularly among children living in low-income and middle-income countries (LMICs). Several Shigella vaccine candidates are advancing in clinical trials and offer promise. Although multiple target populations might benefit from a Shigella vaccine, the primary strategic goal of WHO is to accelerate the development and accessibility of safe, effective, and affordable Shigella vaccines that reduce mortality and morbidity in children younger than 5 years living in LMICs. WHO consulted with regulators and policy makers at national, regional, and global levels to evaluate pathways that could accelerate regulatory approval in this priority population. Special consideration was given to surrogate efficacy biomarkers, the role of controlled human infection models, and the establishment of correlates of protection. A field efficacy study in children younger than 5 years in LMICs is needed to ensure introduction in this priority population.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".