Escherichia coli: placing resistance to third-generation cephalosporins and fluoroquinolones in Australia and New Zealand into perspective
Bibliographic record
Abstract
At least 300 million urinary tract infections (UTIs) occur annually worldwide. Uropathogenic Escherichia coli (UPEC) are the leading cause of UTIs. The discovery of antibiotics has revolutionised modern medicine. Yet, overusing antibiotics has accelerated the emergence of antimicrobial resistance (AMR), with UPEC driving the dissemination of AMR globally. Resistance to broad-spectrum antibiotics like third-generation cephalosporins (3GCs) and fluoroquinolones threatens public health. Extended-spectrum β-lactamase (ESBL)-producing E. coli precipitate resistance, particularly when these antibiotics are used as empirical therapies against UPEC. In response, the Centers for Disease Control and Prevention in the United States have listed ESBL-producing Enterobacterales, such as E. coli as a severe threat. Additionally, the World Health Organization have classified 3GCs and fluoroquinolones as the highest priority (critically important antimicrobials), where these therapies are only recommended following susceptibility testing. The present report demonstrates the distributions of E. coli cases with resistance to 3GC and fluoroquinolones in Australia and New Zealand and contextualises trends with European reports. This investigation emphasises the value of epidemiology and the justification of evidence-based interventions using data as an essential resource for reducing resistance to our ‘first-line’ antibiotics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".