ESBL-positive <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i> isolates from across Canada: CANWARD surveillance study, 2007–18
Bibliographic record
Abstract
OBJECTIVES: ESBL-producing Escherichia coli and Klebsiella pneumoniae are pathogens of increasing importance in Canada and elsewhere in the world. The purpose of this study was to phenotypically and molecularly characterize ESBL-producing E. coli and K. pneumoniae clinical isolates obtained from patients attending Canadian hospitals over a 12 year period. METHODS: Isolates were collected between January 2007 and December 2018 as part of an ongoing national surveillance study (CANWARD). ESBL production was confirmed using the CLSI (M100) phenotypic method. Susceptibility testing was carried out using custom broth microdilution panels, and all isolates underwent WGS. RESULTS: In total, 671 E. coli and 141 K. pneumoniae were confirmed to be ESBL producers. The annual proportion of ESBL-producing isolates increased for both E. coli (from 3.3% in 2007 to 11.2% in 2018; P < 0.0001) and K. pneumoniae (from 1.3% in 2007 to 9.3% in 2018; P < 0.0001). The most frequent STs were ST131 for E. coli [62.4% (419/671) of isolates] and ST11 [7.8% (11/141)] and ST147 [7.8% (11/141)] for K. pneumoniae. Overall, 97.2% of ESBL-producing E. coli and K. pneumoniae isolates were MDR. blaCTX-M-15 predominated in both ESBL-producing E. coli (62.3% of isolates) and ESBL-producing K. pneumoniae (48.9% of isolates). CONCLUSIONS: The proportion of ESBL-producing E. coli, especially ST131, and K. pneumoniae, especially ST11 and ST147, in Canada increased significantly from 2007 to 2018. Continued prospective surveillance of these evolving MDR and at times XDR pathogens is imperative.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".