Changing trends in β-hemolytic streptococcal bacteremia in Manitoba, Canada: 2007–2012
Bibliographic record
Abstract
OBJECTIVES: European surveillance studies have reported an increasing incidence of β-hemolytic group G streptococcal bacteremia, but no studies have evaluated trends in β-hemolytic streptococcal bacteremia in North America. METHODS: We reviewed bacteremic episodes and positive throat swab cultures from two tertiary care centers in Manitoba, Canada, from January 2007 to December 2012. RESULTS: During the study period, 19 864 bacteremic episodes, and 9948 positive throat swabs were identified. There were 1025 (5.16%) bacteremic episodes attributable to β-hemolytic streptococci: 425 (2.03%), 339 (1.71%), 62 (0.31%), and 199 (0.95%) to β-hemolytic groups A, B, C, and G streptococci, respectively. From 2007 to 2012, there were significant increases in the proportion of bacteremia attributable to β-hemolytic streptococci in general (6.32% vs. 4.02%; p<0.0001; linear trend test, p<0.0001), and to groups G (1.49% vs. 0.43%; p<0.0001; linear trend test, p<0.0001) and C (0.58% vs. 0.13%; p=0.0068; linear trend test, p=0.0105) β-hemolytic streptococci in particular. Bacteremia attributable to groups A and B β-hemolytic streptococci and Streptococcus pneumoniae were unchanged. There were no changes in the distribution of β-hemolytic streptococcal groups among throat swabs. CONCLUSIONS: Bacteremia attributable to β-hemolytic groups G and C streptococci increased in Manitoba, Canada. Further study of the factors underlying these changes is required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".