An unusual case of Erysipelothrix rhusiopathiae prosthetic joint infection from the Canadian Arctic: whole genome sequencing unable to identify a zoonotic source
Bibliographic record
Abstract
BACKGROUND: Erysipelothrix rhusiopathiae is a zoonotic pathogen that causes erysipeloid and is most frequently associated with exposure to domestic swine. Infection of native and prosthetic joints is a rarely reported manifestation. CASE PRESENTATION: We describe a case of E. rhusiopathiae prosthetic joint infection in a woman with a history of exposure to wild animals in the Canadian Arctic. Patient management involved a 1-stage surgical revision exchange with an antibiotic impregnated cement spacer and 6 weeks of intravenous penicillin G followed by 6 weeks of oral amoxicillin. Ten previously reported cases of E. rhusiopathiae joint infection are reviewed. Recent increases in mortality due to infection with this organism among host animal populations in the Canadian Arctic have generated concern regarding a potential increase in human infections. However, whole genome sequencing (WGS) of the organism was unable to identify a zoonotic origin for this case. CONCLUSIONS: Consideration should be given to E. rhusiopathiae as a cause of joint infections if the appropriate epidemiologic and host risk factors exist. Expanded use of WGS in other potential animal hosts and environmental sources may provide important epidemiologic information in determining the source of human infections.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".