Exploring Oral Candidiasis in Hemodialysis Patients: <i>Candida</i> Species and Associated Risk Factors
Bibliographic record
Abstract
BACKGROUND: Dialysis patients, often immunocompromised, are highly susceptible to infectious diseases, including oral candidiasis, the most common mycotic infection of the oral mucosa. OBJECTIVE: To identify risk factors for oral candidiasis in dialysis patients to improve management strategies. METHODS: A cross-sectional study was conducted in Birjand, Iran, involving 158 dialysis patients. Oral samples were cultured on CHROMagar Candida medium, with Candida species identified using colony color and molecular techniques. Multiple logistic regression analysis was used to determine predictors of oral candidiasis. RESULTS: Of the 158 patients, 69 (43.7%) tested positive for oral candidiasis, yielding 97 Candida isolates. Candida albicans was the most prevalent species (55.7%), followed by Candida glabrata (22.7%). Significant predictors of oral candidiasis included longer dialysis duration (OR = 7.48; 95% CI = 1.31-2.05; p < 0.001), male gender (OR = 3.07; 95% CI = 1.12-8.39; p = 0.02), and smoking (OR = 7.48; 95% CI = 1.98-28.31; p = 0.003). CONCLUSION: Oral candidiasis is a prevalent opportunistic infection in dialysis patients, particularly among men, smokers, and those with extended dialysis duration. Enhanced screening, especially in developing countries, may be useful to address this often-overlooked condition.
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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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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".