Identification of Factors Impacting Recurrent Clostridium difficile Infection and Development of a Risk Evaluation Tool
Bibliographic record
Abstract
PURPOSE: Recurrent Clostridium difficile infection (RCDI) is a growing concern, yet limited data exists to clarify which patients are at highest risk. Identification of these patients may better inform decisions of those who may benefit from prophylactic intervention. The purpose of this study was to determine which factors are associated with the recurrence of Clostridium difficile infection (CDI) and to develop a risk stratification tool. Methods. Patients readmitted within 10 weeks of positive C. difficile polymerase chain reaction (PCR) with symptoms were included in this retrospective case control study. The primary outcome was analyzed via univariate regression analyses of the independent factors including age, gender, number of CDI episodes, administration of acid blocking agents, antibiotics or chemotherapy, Charlson Comorbidity Index, gastrointestinal conditions, and exposure to healthcare facilities. Results. Recurrent CDI was identified in 44 of 220 included patients. In the univariate analysis, factors associated with development of RCDI included antibiotic exposure (OR 2.51, 95% CI 1.14-5.54; p 0.02) and inflammatory bowel disease (OR 5.77, 95% CI 1.24-26.79; p 0.03). An evaluation tool was created from a well-fit model. Additional factors included in the tool were chosen based on evaluation of findings from existing literature. Conclusions. Antibiotic therapy and inflammatory bowel disease were found to be associated with RCDI. Although a statistically significant association with RCDI was not found for other factors, this is likely related to small sample size. The creation of an evaluation tool using specific patient factors can help determine the risk of RCDI, while future studies may validate this tool. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".