Streptococcus dysgalactiae subsp. equisimilis bacteremia: Emm types and clinical characteristics—a 4-year prospective study
Bibliographic record
Abstract
OBJECTIVES: The incidence of Streptococcus dysgalactiae subspecies equisimilis (SDSE) bacteremia is increasing worldwide, yet studies linking emm types to clinical data remain limited. This study aimed to investigate associations between emm types, clinical manifestations, and disease severity in patients with SDSE bacteremia. METHODS: We prospectively studied 159 SDSE bacteremia episodes in Pirkanmaa, Finland (November 2015 to November 2019). Severe disease was defined as intensive care unit treatment and/or death within 30 days of hospital admission. RESULTS: While emm type stG480 has remained the most common, stG62647 has increased over tenfold to become the second most prevalent. Emm type stC74a was associated with intensive care unit treatment (odds ratio [OR] 5.8 [95% confidence interval (CI) 1.3-26)]), severe disease (OR 5.2 [95% CI 1.4-19]), and predominance of male patients (OR 8.5 [95% CI 1.1-67]). Surgical interventions were linked to emm type stG62647 (OR 2.8 [95% CI 1.1-7.3]). Potential associations between emm type and clinical manifestations were observed: stG643 with endocarditis, stG62647 with foreign-body infections, stG2078 with abscesses, and stG485 with unknown focus. CONCLUSIONS: These results emphasize the importance of emm types in connection with disease severity and clinical manifestations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".