Early-onset neonatal sepsis: Organism patterns between 2009 and 2014
Bibliographic record
Abstract
Abstract Objective To evaluate trends in organisms causing early-onset neonatal sepsis (EONS). Congruent with recent reports, we hypothesized there would be an increase in EONS caused by Escherichia coli. Study Design National data on infants admitted to neonatal intensive care units from 2009 to 2014 were compared to previously reported data from 2003 to 2008. We report 430 cases of EONS from 2009 to 2014. Bivariate analyses were used to analyze the distribution of causative organisms over time and differences by gestational age. Linear regression was used to estimate trends in causative organisms. Results Since 2003, there has been a trend of increasing numbers of cases caused by E coli (P<0.01). The predominant organism was E coli in preterm infants and Group B Streptococcus in term infants. Conclusions With the majority of EONS cases now caused by E coli, our findings emphasize the importance of continued surveillance of causative organism patterns and developing approaches to reduce cases caused by E coli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".