Trends in the Prevalence of <i>Clostridioides difficile</i> Using Real Time PCR in South Korea (2020-2024)
Bibliographic record
Abstract
Clostridioides difficile is a major cause of infection in healthcare settings, and frequent antibiotic use exacerbates its occurrence. C. difficile infection (CDI) is a major public-health challenge owing to its complications and high mortality rate. We aimed to analyze current trends in C. difficile prevalence. This study was a retrospective, single center analysis of 7,371 samples from April 2020 to March 2024. Toxigenic C. difficile was identified from stool samples of inpatients and outpatients using an Xpert C. difficile assay by detecting binary toxin and toxin B via real-time polymerase chain reaction. Prevalence rates were analyzed according to sex, time of occurrence, and age using SPSS Version 29. The chi-square test and the exact test Monte Carlo method were used. Cross-analyses revealed no significant correlation between the occurrence of CDI and months, as well as individual quarters. However, when aggregating data from the same quarter across different years, significant correlations were observed (p = 0.036). Sex-specific prevalence analysis revealed positivity rates of 16.9% and 15.6% in males and females, respectively; however, this difference was not statistically significant. The analysis of prevalence by age did not show a statistically significant difference in cross-analysis. Nevertheless, there was a trend indicating that the positivity rate increased with advancing age. Specifically, the average age of patients with positive results was 72.6 years. This study offers baseline data on the current prevalence of CDI, serving as a valuable resource for hospitals in developing infection control plans and prevention strategies. Additionally, it provides critical insights into CDI epidemiology in South Korea, particularly the increased vulnerability to infection among older age groups. These findings emphasize the need for customized treatment and prevention strategies tailored to the older population, contributing to more effective healthcare interventions and improved patient outcomes.
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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 | 0.001 | 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".