Could a Shigella vaccine impact long-term health outcomes?: Summary report of an expert meeting to inform a Shigella vaccine public health value proposition, March 24 and 29, 2021
Bibliographic record
Abstract
Shigellosis is a leading cause of diarrhea and dysentery in young children from low to middle-income countries and adults experiencing traveler’s diarrhea worldwide. In addition to acute illness, infection by Shigella bacteria is associated with stunted growth among children, which has been linked to detrimental long-term health, developmental, and economic outcomes. On March 24 and 29, 2021, PATH convened an expert panel to discuss the potential impact of Shigella vaccines on these long-term outcomes. Based on current empirical evidence, this discussion focused on whether Shigella vaccines could potentially alleviate the long-term burden associated with Shigella infections. Also, the experts provided recommendations about how to best model the burden, health and vaccine impact, and economic consequences of Shigella infections. This international multidisciplinary panel included 13 scientists, physicians, and economists from multiple relevant specialties. According to the panel, while the relationship between Shigella infections and childhood growth deficits is complex, this relationship likely exists. Vaccine probe studies are the crucial next step to determine whether vaccination could ameliorate Shigella infection - related long-term impacts. Infants should be vaccinated during their first year of life to maximize their protection from severe acute health outcomes and ideally reduce stunting risk and subsequent negative long-term developmental and health impacts. With vaccine schedule crowding, targeted or combination vaccination approaches would likely increase vaccine uptake in high-burden areas. Shigella impact and economic assessment models should include a wider range of linear growth outcomes. Also, these models should produce a spectrum of results—ones addressing immediate benefits for usual health care decision-makers and others that include broader health impacts, providing a more comprehensive picture of vaccination benefits. While many of the underlying mechanisms of this relationship need better characterization, the remaining gaps can be best addressed by collecting data post-vaccine introduction or through large trials.
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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 | 0.028 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".