Characteristics of Patients With Inflammatory Bowel Disease Who Develop Bloodstream Infection
Bibliographic record
Abstract
Background: The causative microorganisms of bloodstream infections (BSIs) in patients with inflammatory bowel disease (IBD) and the clinical characteristics of these patients have not yet been fully identified. Therefore, this study investigated IBD patients who developed BSI to determine their clinical characteristics and identify the BSI-causing bacteria. Methods: The subjects were IBD patients who developed bacteremia between 2015 and 2019 at Fukuoka University Chikushi Hospital. The patients were divided into two groups according to IBD type (Crohn's disease (CD) or ulcerative colitis (UC)). The medical records of the patients were reviewed to determine their clinical backgrounds and identify the BSI-causing bacteria. Results: In total 95 patients, 68 CD and 27 UC patients were included in this study. The detection rates of Pseudomonas aeruginosa ( P. aeruginosa ) and Klebsiella pneumoniae ( K. pneumoniae ) were higher in the UC group than in the CD group (18.5% vs. 2.9%, P = 0.021; 11.1% vs. 0%, P = 0.019, respectively). Immunosuppressive drugs use was higher in the CD group than in the UC group (57.4% vs. 11.1%, P = 0.00003). Hospital stay length was longer in the UC group than in the CD group (15 vs. 9 days; P = 0.045). Conclusions: The causative bacteria of BSI and clinical backgrounds differed between patients with CD and UC. This study showed that P. aeruginosa and K. pneumoniae had higher abundance in UC patients at the onset of BSI. Furthermore, long-term hospitalized patients with UC required antimicrobial therapy against P. aeruginosa and K. pneumoniae. J Clin Med Res. 2023;15(5):262-267 doi: https://doi.org/10.14740/jocmr4920
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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 | 0.000 | 0.002 |
| 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".