Antibiotic Discontinuation 24 h After Neonatal Late-Onset Sepsis Work-Up—A Validated Decision Tree Model
Bibliographic record
Abstract
Objectives: Neonatal late-onset sepsis work-up is a frequent occurrence in every neonatal department. Blood cultures are the diagnostic gold standard, however, a negative culture prior to 48–72 h is often considered insufficient to exclude sepsis. We aimed to develop a decision tree which would enable exclusion of late-onset sepsis within 24 h using clinical and laboratory variables. Study Design: Infants evaluated for late-onset sepsis during the years 2016–2019, without major malformations, in a tertiary neonatal center were eligible for inclusion. Blood cultures and clinical and laboratory data were extracted at 0 and 24 h after sepsis work-up. Infants with bacteriologically confirmed late-onset sepsis were compared to matched control infants. Univariate logistic regression identified potential risk factors. A decision tree based on Chi-square automatic interaction detection methodology was developed and validated. Results: The study cohort was divided to a development cohort (105 patients) and a validation cohort (60 patients). At 24 h after initial evaluation, the best variables to identify sepsis were C-reactive protein > 0.75 mg/dl, neutrophil-to-lymphocyte ratio > 1.5 and sick-appearance at 24 h. Use of these 3 variables together with blood culture status at 24 h, enabled identification of all infants that eventually developed sepsis through the decision tree model. Our decision tree has an area under the receiver operating characteristic curve of 0.94 (95% CI: 0.90–0.98). Conclusions: In non-sick appearing infants with a negative blood culture at 24 h and normal laboratory values, sepsis is highly unlikely and discontinuing antibiotics after 24 h is a viable option.
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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 | 0.001 |
| 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".