Quinolone Use as a Risk Factor for Nosocomial <i>Clostridium difficile-Associated</i> Diarrhea
Bibliographic record
Abstract
OBJECTIVE: To determine modifiable risk factors for nosocomial Clostridium difficile-associated diarrhea (CDAD). DESIGN: Case-control study. SETTING: 300-bed tertiary-care hospital. PARTICIPANTS: Hospital inpatients present during the 3-month study period. METHODS: Case-patients identified with nosocomial CDAD over the study period were compared to two sets of control patients: inpatients matched by age, gender, and date of admission; and inpatients matched by duration of hospital stay. Variables including demographic data, comorbid illnesses, antibiotic exposure, and use of gastrointestinal medications were assessed for case- and control-patients. Conditional logistic regression was performed to identify risk factors for nosocomial CDAD. RESULTS: 27 case-patients were identified and were compared to the two sets of controls (1:1 match for each comparison set). For the first set of controls, use of ciprofloxacin (odds ratio [OR], 5.5; 95% confidence interval [CI 95], 1.2-24.8; P=.03) was the only variable that remained significant in the multivariable model. For the second set of controls, prior exposure to cephalosporins (OR, 6.7; CI 95, 1.3-33.7; P=.02) and to ciprofloxacin (OR, 9.5; CI 95, 1.01-88.4; P=.05) were kept in the final model. CONCLUSIONS: Along with cephalosporins, prior quinolone use predisposed hospitalized patients to nosocomial CDAD. Quinolones should be used judiciously in acute-care hospitals, particularly in those where CDAD is endemic.
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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 | 0.003 |
| 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) | 0.001 | 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".