Minimum Duration of Antibiotic Treatment Based on Blood Culture in Rule Out Neonatal Sepsis
Bibliographic record
Abstract
BACKGROUND: Neonatologists usually wait 48 hours for blood culture results before deciding to discontinue antibiotics. The objective of the study was to analyze time to positive blood culture in rule out sepsis and estimate the minimum duration of antibiotics. METHODS: Retrospective analysis of blood culture at the Neonatal Intensive Care Unit, McMaster Children's Hospital (January 2004 to December 2013) using BacT/Alert® 3D microbial system was conducted. We calculated average time taken for blood culture samples to emit a positive signal and compared it between Gram-positive and Gram-negative organisms. Kaplan-Meier curves for time to detect positive culture were generated. A Cox proportional hazard regression model with the outcome variable "time to detect positive blood culture" and predictor variables "early-onset sepsis (EOS) versus late-onset sepsis (LOS)", "Gram-positive versus Gram-negative" and "definite versus possible pathogen versus contaminant" was generated. RESULTS: Of 7,480 blood cultures performed in 9,254 neonates, 885 samples grew microorganisms. 845 culture reports from 627 neonates were analyzed. Definite or opportunistic pathogens caused 815 (96%) infections (54 EOS and 791 LOS) and the rest were contaminants. Gram-negative organisms grew significantly faster than Gram-positive (P < 0.001). Cultures from EOS were positive significantly earlier than LOS (P = 0.032). Gram-negative status was an independent predictor of early detection of a positive culture (hazard ratio 3.5 [95% CI, 2.7-4.5] P < 0.001). CONCLUSION: The probability of positive blood culture beyond 24 hours for a Gram-negative organism is small. Empiric antimicrobial treatment can be reduced after 24 hours to target only Gram-positive organisms in LOS and can be stopped in EOS unless clinical or laboratory parameters strongly suggest sepsis.
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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 | 0.003 | 0.012 |
| 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 | 0.000 |
| 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".