<p>Risk Factors of Extended-Spectrum Beta-Lactamases-Producing <em>Escherichia coli</em> Community Acquired Urinary Tract Infections: A Systematic Review</p>
Bibliographic record
Abstract
Purpose: The prevalence of extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-EC) has been increasing worldwide since the early 2000s. E. coli is found in 70– 90% of community-acquired urinary tract infections (CA-UTIs). We performed a systematic literature review to determine the risk factors for CA-UTI caused by ESBL-EC. Methods: We searched the MEDLINE, Cochrane Library, Embase and Web of Science databases without language or date restriction up to March 2019. Two independent reviewers selected studies with quantified risk factors for CA-UTI due to ESBL-EC, and assessed their quality using the Newcastle-Ottawa Scale. Results: Among the 5,597 studies identified, 16 observational studies (n=12,138 patients) met the eligibility criteria. The included studies were performed in various countries, and 14/16 were published after 2012. The most relevant risk factors for CA-UTI due to ESBL-EC identified were prior use of antibiotics (odds ratio (OR) from 2.2 to 21.4), previous hospitalization (OR: 1.7 to 3.9), and UTI history (OR: 1.3 to 3.8). Two risk factors were related to environmental contamination: travelling abroad, and swimming in freshwater. Conclusion: Our findings could allow adapting empiric antibiotic treatments according to the patient profile. Further studies are needed to quantify the relationships between CA-UTI due to ESBL-EC and the environment. Keywords: multi-drug resistant bacteria, enterobacteria infection, community-acquired infection, risk factor, beta-lactam resistance, systematic review
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".