The Magnitude and Duration of Clostridium difficile Infection Risk Associated with Antibiotic Therapy: A Hospital Cohort Study
Bibliographic record
Abstract
Antibiotic therapy is the principal risk factor for Clostridium difficile infection (CDI), but little is known about how risks cumulate over the course of therapy and abate after cessation. We prospectively identified CDI cases among adults hospitalized at a tertiary hospital between June 2010 and May 2012. Poisson regression models included covariates for time since admission, age, hospitalization history, disease pressure, and intensive care unit stay. Impacts of antibiotic use through time were modeled using 4 measures: current antibiotic receipt, time since most recent receipt, time since first receipt during a hospitalization, and duration of receipt. Over the 24-month study period, we identified 127 patients with new onset nosocomial CDI (incidence rate per 10,000 patient days [IR] = 5.86). Of the 4 measures, time since most recent receipt was the strongest independent predictor of CDI incidence. Relative to patients with no prior receipt of antibiotics in the last 30 days (IR = 2.95), the incidence rate of CDI was 2.41 times higher (95% confidence interval [CI] 1.41, 4.13) during antibiotic receipt and 2.16 times higher when patients had receipt in the prior 1-5 days (CI 1.17, 4.00). The incidence rates of CDI following 1-3, 4-6 and 7-11 days of antibiotic exposure were 1.60 (CI 0.85, 3.03), 2.27 (CI 1.24, 4.16) and 2.10 (CI 1.12, 3.94) times higher compared to no prior receipt. These findings are consistent with studies showing higher risk associated with longer antibiotic use in hospitalized patients, but suggest that the duration of increased risk is shorter than previously thought.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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, 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".