Evaluation of the Risk Factors of Nosocomial Pneumonia and the Prevalence of Antibiotic Resistance in Trauma Patients in Need of Immediate Intervention
Bibliographic record
Abstract
Background:Pneumonia is the most common nosocomial infection reported worldwide in intensive care units. This study aimed to evaluate the risk factors of nosocomial pneumonia and the frequency of antibiotic resistance in trauma patients who need immediate intervention. Patients and Methods:This prospective cohort study was conducted in Shahid Rajaei Trauma Hospital in Shiraz between 2020 and 2021. All the trauma patients who needed immediate intervention (levels 1 and 2 based on the Canadian Emergency Department Triage and Acuity Scale) and had no symptoms of infection were included. Patients who were discharged or died before 48 hours were excluded. Results:The results demonstrated that major trauma (Injury Severity Score [ISS] ≥16), intubation, and use of invasive mechanical ventilation increase nosocomial pneumonia and death rate. The most common causes of nosocomial infections were Acinetobacter baumannii (23%) and coagulase-negative staphylococcus (18.5%). The highest levels of antibiotic resistance were related to cefoxitin, erythromycin, ciprofloxacin, and trimethoprim-sulfamethoxazole. Conclusions:Major trauma, intubation, and invasive mechanical ventilation were the effective factors in the development of nosocomial pneumonia. Continuous monitoring for mentioned risk factors and strict surveillance of antibiotic prescription can decrease the prevalence of nosocomial infections and subsequent deaths.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".