A Systematic and Functional Classification of Streptococcus pyogenes That Serves as a New Tool for Molecular Typing and Vaccine Development
Bibliographic record
Abstract
Streptococcus pyogenes ranks among the main causes of mortality from bacterial infections worldwide. Currently there is no vaccine to prevent diseases such as rheumatic heart disease and invasive streptococcal infection. The streptococcal M protein that is used as the substrate for epidemiological typing is both a virulence factor and a vaccine antigen. Over 220 variants of this protein have been described, making comparisons between proteins difficult, and hindering M protein-based vaccine development. A functional classification based on 48 emm-clusters containing closely related M proteins that share binding and structural properties is proposed. The need for a paradigm shift from type-specific immunity against S. pyogenes to emm-cluster based immunity for this bacterium should be further investigated. Implementation of this emm-cluster-based system as a standard typing scheme for S. pyogenes will facilitate the design of future studies of M protein function, streptococcal virulence, epidemiological surveillance, and vaccine development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".