Genomic Epidemiology of Invasive Methicillin-Resistant <i>Staphylococcus aureus</i> Infections Among Hospitalized Individuals in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Prevention and control of methicillin-resistant Staphylococcus aureus (MRSA) infections remain challenging. In-depth surveillance integrating patient and isolate data can provide evidence to better inform infection control and public health practice. METHODS: We analyzed MRSA cases diagnosed in 2010 (n = 212) and 2016 (n = 214) by hospitals in Ontario, Canada. Case-level clinical and demographic data were integrated with isolate characteristics, including antimicrobial resistance (AMR), classic genotyping, and whole-genome sequencing results. RESULTS: Community-associated MRSA (epidemiologically defined) increased significantly from 23.6% in 2010 to 43.0% in 2016 (P < .001). The MRSA population structure changed over time, with a 1.5× increase in clonal complex (CC)8 strains and a concomitant decrease in CC5. The clonal shift was reflected in AMR patterns, with a decrease in erythromycin (86.7% to 78.4%, P = .036) and clindamycin resistance (84.3% to 47.9%, P < .001) and a >2-fold increase in fusidic acid resistance (9.0% to 22.5%, P < .001). Isolates within both CC5 and CC8 were relatively genetically diverse. We identified 6 small genomic clusters-3 potentially related to transmission in healthcare settings. CONCLUSIONS: Community-associated MRSA is increasing among hospitalized individuals in Ontario. Clonal shifting from CC5 to CC8 has impacted AMR. We identified a relatively high genetic diversity and limited genomic clustering within these dominant CCs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".