Antibiotics and healthcare facility-associated <i>Clostridioides difficile</i> infection: systematic review and meta-analysis 2020 update
Bibliographic record
Abstract
BACKGROUND: Antibiotic use is the most important modifiable risk factor for healthcare facility-associated Clostridioides difficile infection (HCFA-CDI). Previous systematic reviews cover studies published until 31 December 2012. OBJECTIVES: To update the evidence for associations between antibiotic classes and HCFA-CDI to 31 December 2020. METHODS: PubMed, Scopus, Web of Science Core Collection, WorldCat and Proquest Dissertations & Theses were searched for studies published since 1 January 2013. Eligible studies were those conducted among adult hospital inpatients, measured exposure to individual antibiotics or antibiotic classes, included a comparison group and measured the occurrence of HCFA-CDI as an outcome. The Newcastle-Ottawa Scale was used to appraise study quality. To assess the association between each antibiotic class and HCFA-CDI, a pooled random-effects meta-analysis was undertaken. Meta-regression and subgroup analysis was used to investigate study characteristics identified a priori as potential sources of heterogeneity. RESULTS: Carbapenems and third- and fourth-generation cephalosporin antibiotics remain the most strongly associated with HCFA-CDI, with cases more than twice as likely to have recent exposure to these antibiotics prior to developing HCFA-CDI. Modest associations were observed for fluoroquinolones, clindamycin and β-lactamase inhibitor combination penicillin antibiotics. Individual study effect sizes were variable and heterogeneity was observed for most antibiotic classes. CONCLUSIONS: This review provides the most up-to-date synthesis of evidence in relation to the risk of HCFA-CDI associated with exposure to specific antibiotic classes. Studies were predominantly conducted in North America or Europe and more studies outside of these settings are needed.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".