Prevalence and factors associated with early neonatal sepsis in a neonatal intensive care unit in Medellín, Colombia
Bibliographic record
Abstract
Background: Early neonatal sepsis poses a significant public health challenge worldwide, especially in Colombia and Latin America. It remains a leading cause of morbidity and mortality among newborns, particularly affecting those born prematurely or with low birth weights. Despite advancements in care and preventive strategies, the prevalence of sepsis continues to be alarming. Methods: A descriptive study employing retrospective data was conducted in the PROCAREN Neonatal Intensive Care Unit (NICU) from May 2015 to January 2018. The study aimed to assess the prevalence and identify factors associated with early neonatal sepsis. A total of 88 medical records of neonates diagnosed with sepsis, either clinically or microbiologically, were reviewed, excluding those with incomplete records. Results: Of the neonates studied, 55% were male, and 56% resided outside the metropolitan area. Maternal risk factors identified included chorioamnionitis (85.7%) and nearly half (46%) of women did not receive full prenatal care. Neonatal risk factors included pre-term birth (52.3%), low birth weight (49%), and a 5.7% mortality rate due to sepsis. Additionally, 37 neonates exhibited factors associated with early sepsis, with higher prevalence rates of hypoglycemia (29.7%), pneumonia (24.3%), and urinary tract infections (13.5%). Conclusions: Our findings corroborate those in the literature, emphasizing socio-demographic and neonatal risk factors for early neonatal sepsis. Notable maternal factors identified included chorioamnionitis and prolonged rupture of membranes, while the main neonatal factors were pre-term birth and low birth weight. Despite preventive measures, high incidence and mortality rates due to sepsis persist, underscoring the importance of addressing these factors to improve neonatal outcomes.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".