Epidemiology and Pattern of Resistance of Gram-Negative Bacteria Isolated from Blood Samples in Hospitalized Patients: A Single Center Retrospective Analysis from Southern Italy
Bibliographic record
Abstract
Background: Blood culturing remains the mainstream tool to inform an appropriate treatment in hospital-acquired bloodstream infections and to diagnose any bacteremia. Methods: A retrospective investigation on the prevalence of Gram-negative bacteria (GNB) and their resistance in hospitalized patients by age, sex, and units from blood cultures (BCs) was conducted from January 2018 to April 2020 at Sant’Elia hospital, Caltanissetta, southern Italy. We divided the patient age range into four equal intervals. Results: Multivariate demographic and microbiological variables did not show an association between bacteria distributions and gender and age. The distribution by units showed a higher prevalence of Klebsiella pneumoniae and Acinetobacter baumannii in the intensive care unit (ICU) and Escherichia coli in the non-intensive care units (non-ICUs). The analysis of antibiotic resistance showed that E. coli was susceptible to a large class of antibiotics such as carbapenem and trimethoprim-sulfamethoxazole. K. pneumoniae showed a significant susceptibility to colistin, tigecycline, and trimethoprim-sulfamethoxazole. From the survival analysis, patients with E. coli had a higher survival rate. Conclusions: The authors stress the importance of the implementation of large community-level programs to prevent E. coli bacteremia. K. pneumoniae and E. coli susceptibility patterns to antibiotics, including in the prescription patterns of general practitioners, suggest that the local surveillance and implementation of educational programs remain essential measures to slow down the spread of resistance and, consequently, increase the antibiotic lifespan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".