The Fifth International Neonatal and Maternal Immunization Symposium (INMIS 2019): Securing Protection for the Next Generation
Bibliographic record
Abstract
Despite significant progress in reaching some milestones of the United Nations Sustainable Development Goals, neonatal and early infant morbidity and mortality remain high, and maternal health remains suboptimal in many countries. Novel and improved preventative strategies with the potential to benefit pregnant women and their infants are needed, with maternal and neonatal immunization representing effective approaches. Experts from immunology, vaccinology, infectious diseases, clinicians, industry, public health, and vaccine-related social sciences convened at the 5th International Neonatal and Maternal Immunization Symposium (INMIS) in Vancouver, Canada, from 15 to 17 September 2019. We critically evaluated the lessons learned from recent clinical studies, presented cutting-edge scientific progress in maternal and neonatal immunology and vaccine development, and discussed maternal and neonatal immunization in the broader context of infectious disease epidemiology and public health. Focusing on practical aspects of research and implementation, we also discussed the safety, awareness, and perception of maternal immunization as an existing strategy to address the need to improve maternal and neonatal health worldwide. The symposium provided a comprehensive scientific and practical primer as well as an update for all those with an interest in maternal and neonatal infection, immunity, and vaccination. The summary presented here provides an update of the current status of progress in maternal and neonatal immunization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".