Reducing the Risk of Severe Complications among Patients with<i>Clostridium difficile</i>Infection
Bibliographic record
Abstract
BACKGROUND: The incidence and severity of Clostridium difficile infections are increasing, and there is a need to optimize the prevention of complicated disease. OBJECTIVE: To identify modifiable processes of care associated with an altered risk of C difficile complications. METHODS: A retrospective cohort study (with prospective case ascertainment) of all C difficile infections during 2007⁄2008 at a tertiary care hospital was conducted. RESULTS: Severe complications were frequent (occurring in 97 of 365 [27%] C difficile episodes), with rapid onset (median three days postdiagnosis). On multivariable analysis, nonmodifiable predictors of complications included repeat infection (OR 2.67), confusion (OR 2.01), hypotension (OR 0.97 per increased mmHg) and elevated white blood cell count (OR 1.04 per 109 cells⁄L). Protection from complications was associated with initial use of vancomycin (OR 0.24); harm was associated with ongoing use of exacerbating antibiotics (OR 3.02). CONCLUSION: C difficile infections often occur early in the disease course and are associated with high complication rates. Clinical factors that predicted a higher risk of complications included confusion, hypotension and leukocytosis. The most effective ways to improve outcomes for patients with C difficile colitis are consideration of vancomycin as first-line treatment for moderate to severe cases, and the avoidance of unnecessary antibiotics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, 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".