Duration of Initial Empirical Antibiotic Therapy and Outcomes in Very Low Birth Weight Infants
Bibliographic record
Abstract
BACKGROUND: Overuse of antibiotics can facilitate antibiotic resistance and is associated with adverse neonatal outcomes. We studied the association between duration of antibiotic therapy and short-term outcomes of very low birth weight (VLBW) (<1500 g) infants without culture-proven sepsis. METHODS: We included VLBW infants admitted to NICUs in the Canadian Neonatal Network between 2010-2016 who were exposed to antibiotics but did not have culture-proven sepsis in the first week. Antibiotic exposure was calculated as the number of days an infant received antibiotics in the first week of life. Composite primary outcome was defined as mortality or any major morbidity (severe neurologic injury, retinopathy of prematurity, necrotizing enterocolitis, chronic lung disease, or hospital-acquired infection). RESULTS: = 5856) received 0, 1 to 3, and 4 to 7 days of antibiotics, respectively. Antibiotic exposure for 4 to 7 days was associated with higher odds of the composite outcome (adjusted odds ratio 1.24; 95% confidence interval [CI] 1.09-1.41). Each additional day of antibiotic use was associated with 4.7% (95% CI 2.6%-6.8%) increased odds of composite outcome and 7.3% (95% CI 3.3%-11.4%) increased odds in VLBW infants at low risk of early-onset sepsis (born via cesarean delivery, without labor and without chorioamnionitis). CONCLUSIONS: Prolonged empirical antibiotic exposure within the first week after birth in VLBW infants is associated with increased odds of the composite outcome. This practice is a potential target for antimicrobial stewardship.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".