Isolation of Clostridioides difficile from a Large Animal Veterinary Teaching Hospital Environment
Bibliographic record
Abstract
In veterinary hospitals, the risk of C. difficile nosocomial acquired infections remains largely unknown, and only a few studies surveyed the environmental prevalence of C. difficile in these facilities. The aim of this study was to determine the prevalence of C. difficile in the Ontario Veterinary College large animal hospital environment and to characterize the recovered isolates. Methods. The environment of the large animal clinic of a university veterinary hospital was tested for the presence of C. difficile. Samples were collected from 157 surface sites and cultured using selective enriched broth and selective agar media. Multiplex PCR method for the detection of C. difficile toxin A (tcdA), toxin B (tcdB) binary toxin (cdtA⁄cdtB) genes; high-resolution capillary gel-based electrophoresis PCR-Ribotyping; multilocus sequence typing (MLST) and antimicrobial resistance predictions from sequenced genome were performed. Results. Thirteen isolates were recovered from 157 (8.3%) of multiple sampled sites of the main hospital. Ten distinct ribotypes, of which 7 were positive for toxin genes A and B, and all were negative for binary toxin genes. The two most common PCR ribotypes were 014 and 010. Isolates belong to the MLST Clade 1 and were further divided into 5 different sequence types. A high prevalence of AMR genes was observed in some isolates. Conclusions. C. difficile is present in different areas of the large animal hospital environment, particularly areas of high traffic and surfaces difficult to clean. Active surveillance and biosecurity measures should be in place to maintain a low environmental contamination and prevent nosocomial 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".