Prevalence and Antibiotic Resistance Pattern of Acinetobacter Isolated from Patients Admitted in ICUs in Mazandaran, Northern Iran
Bibliographic record
Abstract
BACKGROUND & PURPOSE: Antibiotic resistance rate is increasing in Acinetobacter species, especially in Acinetobacter baumannii, as the most important pathogen of hospital and ICU . This research aimed to evaluate antibiotic resistant rate of Acinetobacter spp. isolated from patients admitted to ICUs in educational hospitals affiliated with Mazandaran University of Medical Sciences. METHODS: In this cross-sectional descriptive study, 50 Acinetobacter isolates were collected during 2013- 2014. After confirming Acinetobacter species, antibacterial sensitivity test was done using disc diffusion method and minimal inhibitor concentration (MIC) was evaluated by E-test in all isolates. RESULTS: Disc diffusion method revealed that 100% of isolates were resistant to Amikacin and Cefepim and 96% were resistant to both Meropenem and Ciprofloxacin antibiotics, 6% were sensitive, 18% were intermediate and 76% were resistant to imipenem. Also, 84% of isolates were sensitive and 16% were resistant to colistin. In E-test method, 92% of isolates were sensitive and 8% were resistant to colistin. Moreover, an isolate was sensitive, one was intermediate and the remaining isolates were resistant to ciprofloxacin, and 100% of isolates were resistant to other antibiotics in E-test. Over 96% of Acinetobacter isolates were resistant to the antibiotics frequently used in ICU (ciprofloxacin, meropenem, amikacin, and cefepim). Colistin was found as the only appropriate antibiotic that could be used for patients in ICU. CONCLUSION: We hope these results could change the attitude of physicians toward using antibiotics in ICUs and encourage them to follow antibiotic stewardship as the only effective strategy to somewhat control antibiotic resistances.
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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 | 0.001 |
| 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".