The current state of pertussis vaccination in pregnancy around the world, with recommendations for improved care: Consensus statements from the Global Pertussis Initiative
Bibliographic record
Abstract
Bordetella pertussis, which causes a respiratory disease known as pertussis ("whooping cough") remains an important global challenge, with the incidence in pertussis cases increasing in recent years. Newborns and infants are at increased risk for severe morbidity and mortality from this bacterium. Vaccination in pregnancy has become an important strategy to both passively transfer immunity as well as prevent infection in pregnant persons, who are a major source of newborn infection, thus attempting to decrease the impact of this serious disease. It is considered safe for the pregnant person, the developing fetus, and the infant, and during the first 3 months of life it has been shown to be highly effective in preventing pertussis. There are a variety of strategies, recommendations, and adherence rates associated with pertussis vaccination in pregnancy around the world. We summarize the 2021 Global Pertussis Initiative Annual Meeting that reviewed the current global status of pertussis vaccination in pregnancy and remaining medical and scientific questions, with a focus on vaccination challenges and strategies for obstetric and gynecologic healthcare providers.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".