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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".