Update on Streptococcus suis Research and Prevention in the Era of Antimicrobial Restriction: 4th International Workshop on S. suis
Bibliographic record
Abstract
Streptococcus suis is a swine pathogen and a zoonotic agent afflicting people in close contact with infected pigs or pork meat. Sporadic cases of human infections have been reported worldwide. In addition, S. suis outbreaks emerged in Asia, making this bacterium a primary health concern in this part of the globe. In pigs, S. suis disease results in decreased performance and increased mortality, which have a significant economic impact on swine production worldwide. Facing the new regulations in preventive use of antimicrobials in livestock and lack of effective vaccines, control of S. suis infections is worrisome. Increasing and sharing of knowledge on this pathogen is of utmost importance. As such, the pathogenesis and epidemiology of the infection, antimicrobial resistance, progress on diagnosis, prevention, and control were among the topics discussed during the 4th International Workshop on Streptococcus suis (held in Montreal, Canada, June 2019). This review gathers together recent findings on this important pathogen from lectures performed by lead researchers from several countries including Australia, Canada, France, Germany, Japan, Spain, Thailand, The Netherlands, UK, and USA. Finally, policies and recommendations for the manufacture, quality control, and use of inactivated autogenous vaccines are addressed to advance this important field in veterinary medicine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".