Prevalence and Antimicrobial Sensitivity Patterns of Uropathogens, in Tikur Anbessa Specialized Hospital Emergency Medicine Department Addis Ababa, Ethiopia
Bibliographic record
Abstract
Background: Empirical treatment of infections remains a major contributing factor to the emergence of pathogens that are resistant to antibiotics. The study aimed to assess the prevalence and anti-microbial sensitivity patterns of uropathogens in the Emergency Medicine Department of Tikur Anbessa Hospital, Ethiopia. Methods: Urine sample data collected over two years from January 2015 to January 2016 at Tikur Anbessa Hospital's laboratory were retrospectively analyzed for bacterial pathogens, and their antimicrobial susceptibility. Antimicrobial sensitivity tests were done using the disc diffusion technique as per the standard of the Kirby-Bauer method. Results: species (8%). Overall resistance rates to Cotrimoxazole, Ampicillin, Augmentin, and Ceftriaxone were 90.4%, 88.8%, 82.5%, and 79.3%, respectively. The sensitivity rates for Chloramphenicol, Amikacin, Vancomycin, Meropenem, Cefoxitin, and Nitrofurantoin ranged from 72% to 100%. The antibiogram of isolates showed that 43 (86%) isolates were resistant to two or more antimicrobials, and 49 (98%) were resistant to at least one antibiotic. Conclusion and Recommendation: Urinary tract infections are mostly caused by Gram-negative bacteria predominantly in females and Escherichia coli are the most common isolates. Resistance rates to Cotrimoxazole, Ampicillin, Augmentin, and Ceftriaxone were high. Chloramphenicol, Amikacin, Vancomycin, Meropenem, Cefoxitin, and Nitrofurantoin are considered appropriate antimicrobials for the empirical treatment of complicated urinary tract infections in the emergency department. Yet, using antibiotics indiscriminately for patients with complicated UTIs may increase the resistance rate and also lead to treatment failure, hence the prescriptions should be revised following the culture and sensitivity results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".