Can Procalcitonin Improve Antibiotic Stewardship for Late-Onset Sepsis Evaluations in Neonates?
Bibliographic record
Abstract
BACKGROUND: Procalcitonin (PCT) use is not widespread in the neonatal population during late-onset sepsis evaluations. Minimal data exist on appropriate PCT cutoff levels to treat with antibiotics for neonatal sepsis. New guidelines were implemented in select central Texas neonatal intensive care units for late-onset sepsis (infants older than 72 hours) with recommended PCT cutoff levels for antibiotic administration. PURPOSE: To evaluate antibiotic usage in a local neonatal population following late-onset sepsis workups pre-/postimplementation of a PCT guideline. METHODS: A retrospective pre-/post-quality improvement project using chart review data was performed over 11 months in 2018. Inclusion criteria were infants older than 72 hours of life having a late-onset sepsis workup. The outcome measure is appropriate antibiotic administration, based on laboratory test results or cultures, for infants pre-/post-PCT guidelines. RESULTS: The χ test indicated that the proportion of infants receiving appropriate antibiotics pre-/postinitiation of PCT guidelines did not significantly differ. There is, however, clinical significance with an improvement in the proportion of appropriate antibiotic administration and a decrease in variability. IMPLICATIONS FOR PRACTICE: Using PCT may help the practitioner identify sepsis earlier and more effectively, thereby reducing morbidity and mortality among neonates while improving antibiotic stewardship. IMPLICATIONS FOR RESEARCH: The small sample size in this study and the limited number of neonatal intensive care units limit any inferences. Future research should evaluate the use of PCT in a larger sample across multiple settings.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".