Concerns about Mis-/Overuse of Antibiotics in Neonates Born at ≤32 Weeks Gestational Age in Latin American Neonatal Units: Eight Years of Experience in the EpicLatino Database
Bibliographic record
Abstract
There is considerable variability in the duration of antibiotic in neonatal intensive care units (NICUs) all over the world and is highly dependent on gestational ages (GA).It is difficult to withhold antibiotics in critically ill preterm infants because the possibility of infection is difficult to exclude in these patients and the acuity of illness can progress rapidly with potentially disastrous consequences.Available data encouragingly suggest that the incidence of early onset sepsis (EOS) might be lower in EpicLatino units in Latin America compared with Canadian research network (CNN) in 2022 in <30 weeks, but late onset sepsis (LOS) is more frequent at different GA.However, there is an overall scarcity of detailed information from many countries.The annual reports from EpicLatino database do show a high degree of variability in outcomes and a need for cautious interpretation of these figures.However, we still need to establish clear standards for antibiotic use in premature infants; these drugs are essential for combating infections and saving lives but mis-/overuse can exacerbate the risk of late-onset infections, necrotizing enterocolitis (NEC), bacterial resistance, and increase the cost of care.In this study, we aimed to find information on the patterns of antibiotic use in infants born at ≤32 weeks' gestation in the EpicLatino units during the period 2015-2022.A specifically designed questionnaire was sent to unit medical directors to determine whether the total antibiotic use per unit per 1,000 patient-days correlated with the incidence-rate ratios.This is a datacollecting/descriptive study that it will help us in designing further efforts and choosing the sites for intervention.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".