Meta-Analysis of Antibiotics and the Risk of Community-Associated Clostridium difficile Infection
Bibliographic record
Abstract
The rising incidence of Clostridium difficile infection (CDI) could be reduced by lowering exposure to high-risk antibiotics. The objective of this study was to determine the association between antibiotic class and the risk of CDI in the community setting. The EMBASE and PubMed databases were queried without restriction to time period or language. Comparative observational studies and randomized controlled trials (RCTs) considering the impact of exposure to antibiotics on CDI risk among nonhospitalized populations were considered. We estimated pooled odds ratios (OR) for antibiotic classes using random-effect meta-analysis. Our search criteria identified 465 articles, of which 7 met inclusion criteria; all were observational studies. Five studies considered antibiotic risk relative to no antibiotic exposure: clindamycin (OR = 16.80; 95% confidence interval [95% CI], 7.48 to 37.76), fluoroquinolones (OR = 5.50; 95% CI, 4.26 to 7.11), and cephalosporins, monobactams, and carbapenems (CMCs) (OR = 5.68; 95% CI, 2.12 to 15.23) had the largest effects, while macrolides (OR = 2.65; 95% CI, 1.92 to 3.64), sulfonamides and trimethoprim (OR = 1.81; 95% CI, 1.34 to 2.43), and penicillins (OR = 2.71; 95% CI, 1.75 to 4.21) had lower associations with CDI. We noted no effect of tetracyclines on CDI risk (OR = 0.92; 95% CI, 0.61 to 1.40). In the community setting, there is substantial variation in the risk of CDI associated with different antimicrobial classes. Avoidance of high-risk antibiotics (such as clindamycin, CMCs, and fluoroquinolones) in favor of lower-risk antibiotics (such as penicillins, macrolides, and tetracyclines) may help reduce the incidence of CDI.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.059 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".