Incidence, Risk Factors, and Outcomes of Clostridium difficile Infections in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Kidney transplant recipients (KTR) may be at increased risk for Clostridium difficile infections (CDI) but risk factors and outcomes in this population have not been well studied. METHODS: An observational cohort study was conducted to determine the incidence, risk factors, and outcomes of CDI in KTR. A total of 1816 KTR transplanted between 2000 and 2013 at the Toronto General Hospital were included. Sixty-eight patients developed CDI. Controls were selected at a 4:1 ratio using risk-set sampling, and risk factors were explored using conditional logistic regression models. The impact of CDI on graft outcomes was evaluated using Cox proportional hazards models. RESULTS: The incidence rate of CDI was 0.64 cases/100 person-years. Independent predictors of CDI included antibiotic use (odds ratio [OR], 2.88; 95% confidence interval [CI], 1.35-6.15), increased duration of hospitalization posttransplant (OR, 1.04; 95% CI, 1.02-1.06]), receiving a deceased donor kidney (OR, 2.98; 95% CI, 1.47-6.05), and a history of biopsy-proven acute rejection (OR, 5.82; 95% CI, 2.22-15.26). In the Cox proportional hazards model, CDI was found to be an independent risk factor for the subsequent development of biopsy-proven acute rejection (hazard ratio, 2.18; 95% CI, 1.34-3.55). CONCLUSIONS: Our results confirm that transplant-specific factors place KTR at a higher risk for CDI. Clostridium difficile infections may increase the risk of adverse outcomes, such as biopsy-proven acute rejection. These findings emphasize the importance of preventive strategies to reduce the morbidity associated with CDI in KTR.
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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 | 0.003 |
| 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".