Prevalent emm Types among Invasive GAS in Europe and North America since Year 2000
Bibliographic record
Abstract
Background: Streptococcus pyogenes or group A streptococcus (GAS) is an important human pathogen responsible for a broad range of infections, from uncomplicated to more severe and invasive diseases with high mortality and morbidity. Epidemiological surveillance has been crucial to detect changes in the geographical and temporal variation of the disease pattern; for this purpose the M protein gene (emm) gene typing is the most widely used genotyping method, with more than 200 emm types recognized. Molecular epidemiological data have been also used for the development of GAS M protein-based vaccines. Methods: The aim of this paper was to provide an updated scenario of the most prevalent GAS emm types responsible for invasive infections in developed countries as Europe and North America (US and Canada), from 1st January 2000 to 31st May 2017. The search, performed in PubMed by the combined use of the terms (“emm”) and (”invasive”) retrieved 264 articles, of which 38 articles, (31 from Europe and 7 from North America) met the inclusion criteria and were selected for this study. Additional 5 papers cited in the European articles but not retrieved by the search were included. Results: emm1 represented the dominant type in both Europe and North America, replaced by other emm types in only few occasions. The seven major emm types identified (emm1, emm28, emm89, emm3, emm12, emm4, and emm6) accounted for approximately 50-70% of the total isolates; less common emm types accounted for the remaining 30-50% of the cases. Most of the common emm types are included in either one or both the 26-valent and 30-valent vaccines, though some well-represented emm types found in Europe are not. Conclusion: This study provided a picture of the prevalent emm types among iGAS in Europe and North America since the year 2000 onwards. Continuous surveillance on the emm type distribution among iGAS infections is strongly encouraged also to determine the potential coverage of the developing multivalent vaccines.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".