Efficacy and Safety of Procalcitonin-Guided Decision Making in Neonates Suspected of Early Onset Sepsis: The Neopins Study—An International, Multicenter Non-Inferiority Randomized Controlled Intervention Trial
Bibliographic record
Abstract
Background. Uncertainty about the presence of neonatal early-onset sepsis (EOS) results in unnecessary and prolonged empiric antibiotic treatment. This study evaluates whether Procalcitonin (PCT)-guided treatment for suspected EOS can reduce the duration of antibiotic treatment with unchanged outcome (re-infection/death in the first month of life with 2% margin for non-inferiority). Methods. Randomized controlled intervention trial recruiting neonates (gestational age ≥34 weeks) suspected of EOS requiring antibiotic therapy. Patients were stratified into 4 risk categories and randomized for duration of antibiotic treatment based on PCT-guided decision-making or standard care. Analyses were done as intention-to-treat (ITT) as well as per protocol (PP). Results. A total of 1710 neonates were randomized and included in the ITT analysis, 1408 in the PP analysis. The duration of antibiotic therapy was significantly shorter in the PCT group than in the standard group (ITT: 55.1 versus 65.0 hours, P < 0.001; PP: 40.0 versus 62.0 hours; P < 0.001). Length of hospital stay was significantly (P = 0.0028) reduced in the PCT group with a small effect size (ITT: −3.5 hours; PP: −5.2 hours). No sepsis-related deaths occurred and the rate of possible re-infection was below 1% with a risk difference of 0.1% (exact CI −4.6 to 4.8%). Non-inferiority (margin 2%) could not be statistically proven due to the low occurrence of possible relapse infections. Conclusion. Initial risk assessment for suspected EOS and PCT guidance on duration of empirical antibiotic therapy results in a significant reduction of duration of antibiotic therapy and length of hospital stay. The effect size is dependent on protocol adherence. The approach used seems to be safe whereas non-inferiority cannot be claimed statistically. Disclosures. All authors: No reported disclosures.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".