Genomic Epidemiology of Major Extraintestinal Pathogenic Escherichia coli Lineages Causing Urinary Tract Infections in Young Women Across Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background A few extraintestinal pathogenic Escherichia coli (ExPEC) multilocus sequence types (STs) cause the majority of community-acquired urinary tract infections (UTIs). We examine the genomic epidemiology of major ExPEC lineages, specifically factors associated with intestinal acquisition. Methods A total of 385 women with UTI caused by E. coli across Canada were asked about their diet, travel, and other exposures. Genome sequencing was used to determine both ST and genomic similarity. Logistic regression was used to identify factors associated with the acquisition of and infection with major ExPEC STs relative to minor ExPEC STs. Results ST131, ST69, ST73, ST127, and ST95 were responsible for 54% of all UTIs. Seven UTI clusters were identified, but genomes from the ST95, ST127, and ST420 clusters exhibited as few as 3 single nucleotide variations across the entire genome, suggesting recent acquisition. Furthermore, we identified a cluster of UTIs caused by 6 genetically-related ST1193 isolates carrying mutations in gyrA and parC. The acquisition of and infection with ST69, ST95, ST127, and ST131 were all associated with increased travel. The consumption of high-risk foods such as raw meat or vegetables, undercooked eggs, and seafood was associated with acquisition of and infection with ST69, ST127, and ST131, respectively. Conclusions Reservoirs may aid in the dissemination of pandemic ExPEC lineages in the community. Identifying ExPEC reservoirs may help prevent future emergence and dissemination of high-risk lineages within the community setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it