Treatment of Clostridium difficile infection in community teaching hospital: a retrospective study
Bibliographic record
Abstract
OBJECTIVES: Clostridium difficile infection (CDI) is responsible for 15-25% cases of health-care-associated diarrhea. The CDI treatment algorithm used at our hospital is adapted from the Infectious Diseases Society of America 2010 C. difficile guideline. The primary objective of this study was to assess the treatment adherence to our algorithm; this was defined as therapy consisting of the appropriate antibiotic, dose, route, interval, and duration indicated based on the disease severity and episode within 24 h of diagnosis. Furthermore, our study also described the population and their risk factors for CDI at our hospital. METHODS: This was a single-centre, retrospective cohort chart review of CDI cases that were diagnosed at admission or during hospitalization from June 1st, 2017 to June 30th, 2018. Cases were identified by a positive stool test along with watery diarrhea or by colonoscopy. RESULTS: Sixty cases were included, of which adherence to our algorithm was 50%. Overall, severe CDI had the highest treatment non-adherence (83%), and the biggest contributing factor was prescribing the wrong antibiotic (72%). In severe CDI, which warrants vancomycin monotherapy, wrong antibiotic consisted of metronidazole monotherapy (55%) or dual therapy with metronidazole and vancomycin (45%). Patients were mostly older, females being treated for an initial episode of mild-to-moderate CDI. Common risk factors identified were age over 65 years (80%), use of antibiotics (83%) and proton pump inhibitors (PPI) (68%) within the previous 3 months. The use of a PPI in this study, a modifiable risk factor without a clear indication, was 35%. CONCLUSION: An area for antimicrobial stewardship intervention in CDI treatment at our hospital is prescribing the right antibiotic based on the CDI indication. In severe CDI, an emphasis should be on prescribing vancomycin monotherapy as the drug of choice. PPI use should be reassessed for tapering when appropriate.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".