High Incidence of Invasive Group A Streptococcus Disease Caused by Strains of Uncommon <i>emm</i> Types in Thunder Bay, Ontario, Canada
Bibliographic record
Abstract
An outbreak of type emm59 invasive group A Streptococcus (iGAS) disease was declared in 2008 in Thunder Bay District, Northwestern Ontario, 2 years after a countrywide emm59 epidemic was recognized in Canada. Despite a declining number of emm59 infections since 2010, numerous cases of iGAS disease continue to be reported in the area. We collected clinical information on all iGAS cases recorded in Thunder Bay District from 2008 to 2013. We also emm typed and sequenced the genomes of all available strains isolated from 2011 to 2013 from iGAS infections and from severe cases of soft tissue infections. We used whole-genome sequencing data to investigate the population structure of GAS strains of the most frequently isolated emm types. We report an increased incidence of iGAS in Thunder Bay compared to the metropolitan area of Toronto/Peel and the province of Ontario. Illicit drug use, alcohol abuse, homelessness, and hepatitis C infection were underlying diseases or conditions that might have predisposed patients to iGAS disease. Most cases were caused by clonal strains of skin or generalist emm types (i.e., emm82, emm87, emm101, emm4, emm83, and emm114) uncommonly seen in other areas of the province. We observed rapid waxing and waning of emm types causing disease and their replacement by other emm types associated with the same tissue tropisms. Thus, iGAS disease in Thunder Bay District predominantly affects a select population of disadvantaged persons and is caused by clonally related strains of a few skin and generalist emm types less commonly associated with iGAS in other areas of Ontario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".