Antibiotics Resistance Profile of Uropathogens Isolated from Al Buraimi Hospital, Sultanate of Oman
Bibliographic record
Abstract
BACKGROUND: Urinary tract infections (UTIs) is a worldwide problem of all age groups and gender. Emerging resistance to antibiotics making difficult in the choice of treatment and management of UTI cases. This study sought to determine the gender wise prevalence of common uropathogens from UTI patients and the resistance profile of uropathogens against commonly used antibiotics.METHOD: This cross sectional study was conducted in Al Buraimi Hospital, Sultanate of Oman. The data of UTI patients visited hospital was analyzed for the isolation of uropathogens from positive urine culture and antimicrobial sensitivity test was performed by disc diffusion method. Descriptive statistics was used to analyze the data using SPSS 21.0 and Microsoft Excel.RESULT: Total 4,480 urine samples were analyzed for isolation of uropathogens and significant bacteriuria were found in total 846 (19%) samples. Overall 728 (86%) Gram-negative and 118 (14%) Gram positive uropathogens were isolated from total (846) positive urine samples and the highest prevalence of isolates was observed in females 542(74%) than males 186(26%). E.coli was found the highest prevalent (50.3%) uropathogens followed by Klebsiella species (13.9%), Pseudomonas (6.3%), A.baumannii (4%), E.Cloacae (2.2%), Proteus species (1.4%), Citrobacter species (1.2%), M. morgani (0.3%) and Serratia species (0.1%). The highest (34.3%) antibiotic resistance was noticed in E.coli against Nalidixic Acid, however, susceptiblity was found against Ceftriaxone, Ceftazidime, Ciprofloxacin and Nitrofurantoin among female and males.CONCLUSION: Overall, β- lactam antibiotics, Cephalosporins, Fluroquiolones, Macrobids would be the first line of drugs and the most effective for the empirical treatment of Gram-negative and Gram-positive uropathogens; however Aminoglycosides, Carbapenems and Polymyxin could be used for the treatment of UTI infections as the second choice.
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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 | 0.001 | 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) | 0.002 | 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".