Predictive factors of Clostridioides difficile infection in hospitalized patients with new diarrhea: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Diagnostic testing for Clostridioides difficile infection (CDI) by nucleic acid amplification test (NAAT) cannot distinguish between colonization and infection. A positive NAAT may therefore represent a false positive for infection, since diarrhea due to various aetiologies may occur in hospitalized patients. Our objective was to help answer the question: "does this medical inpatient with diarrhea have CDI?" DESIGN: We conducted a retrospective cohort study (n = 248) on the Clinical Teaching Units of the Royal Victoria Hospital (Montréal, Canada). Patients were included if they had a NAAT between January 2014 and September 2015 and their admission diagnosis was not CDI. CDI cases and non-CDI cases were compared, and independent predictors of CDI were determined by logistic regression. RESULTS: Several factors were independently associated with CDI, including: hemodialysis (OR: 13.5, 95% CI: 2.85-63.8), atrial fibrillation (OR: 3.70, 95% CI: 1.52-9.01), whether the patient received empiric treatment (OR: 3.01, 95% CI: 1.04-8.68), systemic antibiotic therapy prior to testing (OR: 4.23, 95% CI: 1.71-10.5), previous positive NAAT (OR: 3.70, 95% CI: 1.41-9.72), and a leukocyte count of 11x109/L or higher (OR: 3.43, 95% CI: 1.42-8.26). The area under the curve was 0.80. CONCLUSION: For patients presenting with hospital-onset diarrhea, various parameters can help differentiate between CDI and other causes. A clinical prediction calculator derived from our cohort (http://individual.utoronto.ca/leet/cdiff.html) might assist clinicians in estimating the risk of CDI for inpatients; those with low pre-test probability may not require immediate testing, treatment, nor prolonged isolation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".