Increasing Prevalence and Severity of Clostridium difficile Colitis in Hospitalized Patients in the United States
Bibliographic record
Abstract
OBJECTIVE: To evaluate changes in the epidemiological features of Clostridium difficile colitis in hospitalized patients in the United States (C difficile is a common cause of nosocomial diarrhea that has been shown to be increasing in virulence in Canada and across Europe). DESIGN: Cohort analysis of all patients with C difficile colitis in the Nationwide Inpatient Sample. SETTING: Population-based data from the Nationwide Inpatient Sample, a 20% stratified random sample of US hospital discharge abstracts from January 1, 1993, through December 31, 2003. PATIENTS: Using standard International Classification of Diseases, Ninth Revision (ICD-9) diagnostic codes, we identified patients with C difficile colitis. We controlled for comorbid conditions by calculating the Deyo modification of the Charlson score. To determine the relationship of year of diagnosis on main outcome measures, we constructed multivariate models. MAIN OUTCOME MEASURES: The prevalence, case fatality, total mortality rate, and colectomy rate of C difficile colitis. RESULTS: We found that the prevalence, case fatality, total mortality rate, and colectomy rate of C difficile colitis increased from 1993 through 2003. In our regression analysis, the year of diagnosis predicted an increase in prevalence, case fatality, total mortality rate, and colectomy rate after adjusting for potential confounders. CONCLUSIONS: The prevalence, case fatality, total mortality rate, and colectomy rate of C difficile colitis significantly increased from 1993 to 2003. These findings provide compelling evidence of the changing epidemiological features of C difficile colitis.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".