Variation in Current Management of Term and Late-preterm Neonates at Risk for Early-onset Sepsis
Bibliographic record
Abstract
BACKGROUND: Uncertainty about the presence of infection results in unnecessary and prolonged empiric antibiotic treatment of newborns at risk for early-onset sepsis (EOS). This study evaluates the impact of this uncertainty on the diversity in management. METHODS: A web-based survey with questions addressing management of infection risk-adjusted scenarios was performed in Europe, North America, and Australia. Published national guidelines (n = 5) were reviewed and compared with the results of the survey. RESULTS: 439 Clinicians (68% were neonatologists) from 16 countries completed the survey. In the low-risk scenario, 29% would start antibiotic therapy and 26% would not, both groups without laboratory investigations; 45% would start if laboratory markers were abnormal. In the high-risk scenario, 99% would start antibiotic therapy. In the low-risk scenario, 89% would discontinue antibiotic therapy before 72 hours. In the high-risk scenario, 35% would discontinue therapy before 72 hours, 56% would continue therapy for 5-7 days, and 9% for more than 7 days. Laboratory investigations were used in 31% of scenarios for the decision to start, and in 72% for the decision to discontinue antibiotic treatment. National guidelines differ considerably regarding the decision to start in low-risk and regarding the decision to continue therapy in higher risk situations. CONCLUSIONS: There is a broad diversity of clinical practice in management of EOS and a lack of agreement between current guidelines. The results of the survey reflect the diversity of national guidelines. Prospective studies regarding management of neonates at risk of EOS with safety endpoints are needed.
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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 | 0.009 | 0.053 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".