Prevalence of Candida Species Isolates in Patients with Urinary Tract Infection in Madinah, Saudi Arabia
Bibliographic record
Abstract
INTRODUCTION: Urinary tract infections (UTIs) is considered one of the most prevalent infections that may lead to many renal complications or dysfunctions. They are responsible for almost 10% of all infections in Saudi Arabia, which makes them the second most common cause of emergency department admission. Bacteria are the most common pathogens associated with UTIs. Nevertheless, UTIs caused by fungi have also been reported. Among fungal infections, Candida spp. has been identified as the primary cause of UTI-related mycoses. OBJECTIVE: To assess the prevalence of Candida species isolated from adult patients in Madinah, Saudi Arabia. MATERIALS & METHODS: A retrospective cross-sectional study was performed using data collected from patients who visited King Fahad General Hospital in Madinah, Saudi Arabia. A total of 16803 urine bacterial cultures data were collected from January 2019 to October 2021. RESULTS: Among the 4281 positive urine cultures, 92% (n = 3937) showed bacterial growth, while 8% (n = 344) exhibited fungal growth. Female patients had a slightly higher percentage of fungal-positive cases (53%, n = 181) compared to males (47%, n = 163). Among different nationalities, Saudi citizens had the highest prevalence of positive fungal samples (64%, n = 220). Most positive cases (49%, n = 167) were isolated in 2019, with a decline in subsequent years. Non-Candida albicans spp. (86.3%, n = 297) were the most common fungal species, followed by Candida albicans (13.7%, n = 47). CONCLUSION: Despite considering bacterial UTIs to be more prevalent than UTI-related mycoses, it remains crucial to accurately identify the causative agent for proper diagnosis and treatment. UTI-related mycosis caused by non-Candida albicans spp. has significantly increased compared to Candida albicans. Thus, monitoring these trends over time can provide valuable insights for developing preventive strategies and optimizing treatment approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".