Keeping Vulnerable Children Safe from Pertussis Preventing Nosocomial Pertussis Transmission in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of different acellular pertussis booster vaccination strategies on the probability of a nosocomial pertussis outbreak occurring and the distribution of outbreak sizes observed for each intervention strategy. SETTING: Neonatal intensive care unit. METHODS: We developed a stochastic, agent-based simulation model to examine the impact of booster vaccination strategies for pertussis on health care-related transmission. RESULTS: Our results demonstrate that healthcare worker booster vaccination decreases the probability of secondary transmission from 49% (base case, no boosting) to 2% (if 95% of healthcare workers are boosted) and decreases final outbreak size. Boosting family caregivers did not have a clinically significant impact on nosocomial disease transmission. CONCLUSION: The provision of booster vaccine to healthcare workers in the neonatal intensive care unit substantially reduces the risk of hospital-centered pertussis outbreaks in a manner that enhances the health of hospitalized children. A formal health economic analysis of this finding is currently under way. Policies to protect patient safety in pediatric facilities should include compliance with the United States Advisory Committee on Immunization Practices, which recommends provision of pertussis booster vaccination to healthcare workers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".