Brief communication (Original). Trends in neonatal sepsis in a neonatal intensive care unit in Thailand before and after construction of a new facility
Bibliographic record
Abstract
Abstract Background: Neonatal sepsis is a cause of mortality and long-term morbidity worldwide. Objectives: To describe longitudinal trends in the cumulative incidence of early- and late-onset sepsis (EOS and LOS), mortality, and causative organisms in a Thai Hospital before and after construction of a new neonatal intensive care unit (NICU). Methods: Review of NICU admissions with blood cultures positive for bacteria or fungi for the periods 1995 to 2002 (preconstruction) and 2004 to 2010 (postconstruction). Sepsis was categorized into EOS (within first 3 days of life) and LOS (after first 3 days of life). Results: Of 5,570 admissions, 241 (4.3%) neonates with 276 episodes of sepsis were identified. There was no difference in the rate of sepsis overall (P = 0.90), LOS (P = 0.30), or sepsis-related mortality (P = 0.61) over the two periods, but the rate of EOS increased significantly from 0.34% to 0.81% (P = 0.04). Rates of Klebsiella species and Escherichia coli sepsis increased from 13.6% to 25.6% (P = 0.01) and from 5.3% to 12.2% (P = 0.04), respectively, while rates of Staphylococcus aureus sepsis decreased from 12.9% to 4.3% (P < 0.007). Sepsisrelated mortality was 1.8%. Conclusions: Although direct causality cannot be proven, the rate of EOS and the pattern of causative organisms changed following construction of the new NICU. Building a new unit does not necessarily result in a reduction in the rate of sepsis. This data may provide a baseline for implementing evidence-based infection control strategies to prevent/reduce sepsis and improve neonatal care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".