Characterization of 2 <i>Klebsiella pneumoniae</i> carbapenemase–producing Enterobacterales isolated from canine rectal swabs
Bibliographic record
Abstract
Globally, carbapenemase-producing Enterobacterales (CPE) cause life-threatening, hospital-acquired infections in people, and have been reported recently among veterinary patients. Organisms that produce a Klebsiella pneumoniae carbapenemase (KPC) are one of the most common CPE isolated from people but have been reported only rarely in animals. We characterized 2 KPC-producing Enterobacterales isolated from companion animal rectal swabs during the response to an outbreak caused by a strain of blaNDM-5 Escherichia coli. Both isolates were characterized by whole-genome sequencing (WGS) and analysis. The first isolate (case A) was from an immunosuppressed 6-y-old Yorkshire Terrier and was identified as E. coli (ST372) with a blaKPC-18 gene and an IncFII plasmid. The second isolate (case B) was from a 3-y-old Labrador Retriever with acute diarrhea and was identified as Citrobacter koseri with a blaKPC-2 gene, multiple plasmids (ColRNAI, pKPC-CAV1193), and a putative enterotoxin gene (senB). Further research is needed to determine what role animals might play in the epidemiology of CPE in communities. It is imperative that all CPE isolated from companion animals be fully characterized by WGS and the associated case examined. All veterinary isolates should be sequenced and shared for surveillance, monitoring, and investigation purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".