Clostridium difficile Infections Among Hospitalized Individuals With IBD
Bibliographic record
Abstract
Introduction: Much of the information on the epidemiology of Clostridium difficile Infections (CDI) in North America comes from an assessment of hospital discharge databases (DAD), using the International Classification of Diseases code for CDI. We have recently reported hospital discharge abstracts have limited accuracy in identifying occurrence of CDI among hospitalized individuals with IBD. However the impact of this misclassification on the assessment of time trends and CDI epidemiology among individuals with IBD remains unknown. Methods: The University of Manitoba IBD Epidemiology Database was used to identify individuals with and without IBD and DAD CDI diagnosis (07/01/2005-3/31/2014), who were matched on age, sex and area of residence. The Manitoba Health Public Health Branch Epidemiology and Surveillance population-based CDI dataset was used to identify laboratory confirmed CDI cases. Joinpoint Regression program developed by SEER was used to assess the time trends of CDI rates. Cox proportional hazards regression models were used to determine the relative risks (estimated as hazard ratios (HRs) and corresponding confidence intervals (CIs) of CDIs (first episode) among individuals with and without IBD. Nested case control study was performed to determine predictors of CDI among individuals with IBD. Results: CDI assessment from DAD showed an increase in CDI rates among hospitalized individuals with IBD (Annual percent change (APC): 5.31; p=0.02), in contrast to no significant change in the assessment from lab CDI dataset (APC: -1.62; p=0.61) (figure 1). There was no significant change among individuals without IBD (DAD APC: -4.48; p=0.06; Lab dataset APC -6.34; p=0.07). This resulted in apparent increase in rate ratio of CDI among those with IBD vs. those without IBD in the DAD, but not with the Lab dataset (figure 2). DAD assessment suggested a much more increased risk of CDI among young individuals with IBD than the lab CDI dataset (figure 3). The nested case control suggested short duration of IBD (within first year) was a much more marked risk factor for CDI in the DAD analysis (OR 27.1; 95% CI: 7.7-95.9) than in the lab CDI analysis. Conclusion: Results of studies using ICD codes to study epidemiology of Clostridium difficile infections among those with IBD should be viewed with caution. Incidence of Clostridium difficile infections is not increasing among hospitalized patients with IBD.FigureFigureFigure
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".