Incidence of Hospital-Acquired Bacterial Pneumonia and Its Resistance Profiles in Patients Admitted to Intensive Care Unit
Bibliographic record
Abstract
Hospital-acquired bacterial pneumonia (HABP) is one of the most important causes of morbidity, mortality and economic problems especially for patients admitted in the intensive care unit (ICU) ward. The aim of this study was to determine the incidence of nosocomial pneumonia in ICU, identify the causative bacteria and their resistance profiles. This cross sectional study was performed on 214 patients who were admitted in the ICU ward of a general hospital requiring mechanical ventilation for at least 48 h. Identification of HABP was based on the clinical signs manifested 48 h or more after admission, new chest X-ray infiltrates and microbiologic examination of endo tracheal secretion. Data were analyzed using SPSS 21 to perform the descriptive statistics. The isolated gram negative bacteria were Klebsiella pneumoniae (50%), Staphylococcus aureus (18.7%), Acinetobacter baumannii (12.5%), Escherichia coli (12.5%) and Pseudomonas aeroginosa (6.3%). The maximum antimicrobial resistance of gram negative bacteria was to Cefazolin (100%) and Ampicillin (84.6%), while antimicrobial resistance to Clindamycin, Azithromycin, Amoxycillin+clavulanate, Trimethoprim+sulfamethoxazole and Ciprofloxacin was 33.3%. No resistance was seen towards carbapenems.The most frequent gram negative isolated bacterium was K. pneumoniae, and maximum antimicrobial resistance rate was observed for Cefazolin and Ampicillin, which is due to betalactamase production.
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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 | 0.000 | 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) | 0.001 | 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".