Risk Factors Associated With Recurrent <i>Clostridium difficile</i> Infection
Bibliographic record
Abstract
BACKGROUND: infection (CDI) is a problem that can cost up to $20,000 each year in the United States. Studies have reported risk factors that may be associated with a higher incidence of recurrent CDI. We studied additional risk factors, including history of partial colectomy, chemotherapy use and hospitalization in the intensive care unit (ICU). METHODS: We conducted a retrospective chart review of all outpatients and inpatients at our institution to determine risk factors associated with recurrent CDI. Frequencies were compared using Fisher's exact test and continuous data were compared using Wilcoxon ranks sums test. Recurrent CDI was determined for all patients and risk factors were analyzed using single and multiple logistic regression. A P < 0.05 was used to determine significance. RESULTS: This study included 435 patients and found that advanced age significantly increased the odds of recurrent CDI by 2.3% per year (OR = 1.023, 95% CI = 1.009 - 1.037, P < 0.05). Patients with prior partial colectomy were found to have 3.2 times increased odds of recurrence compared to those without history of partial colectomy (OR = 3.168, 95% CI = 1.324 - 7.579, P < 0.05). Patients receiving chemotherapy or hospitalized in the ICU were not found to have a significantly higher rate of recurrent CDI (P > 0.05). CONCLUSIONS: Advanced age and history of partial colectomy were associated with a significantly higher recurrence rate of CDI. Contrary to prior studies, chemotherapy use or hospitalization in the ICU were not found to be associated with a higher rate of recurrent CDI.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".