Antimicrobial Stewardship at Birth in Preterm Infants
Bibliographic record
Abstract
Background: Early-onset sepsis results in increased morbidity and mortality in preterm infants. Antimicrobial Stewardship Programs (ASPs) address the need to balance adverse effects of antibiotic exposure with the need for empiric treatment for infants at the highest risk for early-onset sepsis. Methods: All preterm infants <34 weeks gestational age born during a 6-month period before (January 2017–June 2017) and a 6-month period after (January 2019–June 2019) implementation of ASP in May 2018 were reviewed. The presence of perinatal sepsis risk factors, eligibility for, versus treatment with initial empiric antibiotics was compared. Results: Our cohort comprised 479 infants with a mean of 30 weeks gestation and birth weight of 1400 g. Demographics were comparable, with more Cesarean section deliveries in the post-ASP cohort. Any sepsis risk factor was present in 73.6% versus 68.4% in the pre- versus post-ASP cohorts (P = 0.23). Fewer infants were treated with antibiotics in the later cohort (60.4%) compared with the earlier cohort (69.7%; P = 0.04). Despite the presence of risk factors (preterm labor in 93% and rupture of membranes in 60%), 42% of infants did not receive initial antibiotics. Twenty percent with no perinatal sepsis risk factors were deemed low-risk and not treated. Conclusions: Implementation of a neonatal ASP decreased antibiotic initiation at birth. Antibiotic use decreased (appropriately) in the subgroup with no perinatal sepsis risk factors. Of concern, some infants were not treated despite risk factors, such as preterm labor/rupture of membrane. Neonatal ASP teams need to be aware of potentially unintended consequences of their initiatives.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".