Ulcerative Colitis Patients With Clostridium difficile are at Increased Risk of Death, Colectomy, and Postoperative Complications: A Population-Based Inception Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Clostridium difficile (C. difficile) may worsen the prognosis of ulcerative colitis (UC). The objectives of this study were to: (i) validate the International Classification of Diseases-10 (ICD-10) code for C. difficile; (ii) determine the risk of C. difficile infection after diagnosis of UC; (iii) evaluate the effect of C. difficile infection on the risk of colectomy; and (iv) assess the association between C. difficile and postoperative complications. METHODS: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated by comparing ICD-10 codes for C. difficile with stool toxin tests. A population-based surveillance cohort of newly diagnosed UC patients living in Alberta, Canada were identified from 2003 to 2009 (n=1,754). The effect of a C. difficile infection on colectomy was modeled using competing risk survival regression after adjusting for covariates. The effect of a C. difficile infection on postoperative complications was assessed using a mixed effects logistic regression model. RESULTS: The sensitivity, specificity, PPV, and NPV of the ICD-10 code for C. difficile were 82.1%, 99.4%, 88.4%, and 99.1%, respectively. The risk of C. difficile infection within 5 years of diagnosis with UC was 3.4% (95% confidence interval (CI): 2.5-4.6%). The risk of colectomy was higher among UC patients diagnosed with C. difficile (sub-hazard ratio (sHR)=2.36; 95% CI: 1.47-3.80). C. difficile increased the risk of postoperative complications (odds ratio=4.84; 95% CI: 1.28-18.35). C. difficile was associated with mortality (sHR=2.56 times; 95% CI: 1.28-5.10). CONCLUSIONS: C. difficile diagnosis worsens the prognosis of newly diagnosed patients with UC by increasing the risk of colectomy, postoperative complications, and death.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".