Trend and pattern of antimicrobial resistance in molluscan <i>Vibrio</i> species sourced to Canadian estuaries
Bibliographic record
Abstract
ABSTRACT Emergence of antimicrobial resistance (AMR) in foodborne bacteria is a growing concern worldwide. AMR surveillance is a key element in understanding the implications resulting from the use of antibiotics for therapeutic as well as prophylactic needs. The emergence and spread of AMR in foodborne human pathogens is an indirect health hazard. This surveillance study reports the trend and pattern of AMR detected in Vibrio species isolated from molluscs harvested in Canada, between 2006 and 2012, against 19 commonly used antibiotics. Five common antibiotics, ampicillin, cephalothin, erythromycin, kanamycin and streptomycin, predominantly contributed to AMR including multi-drug resistance (MDR) in the molluscan Vibrio spp. isolated in 2006. A prospective follow-up analysis of these drugs showed a declining trend in the frequency of MDR/AMR- Vibrio spp. in subsequent years until 2012. The observed decline appears to have been influenced by the specific downturn in resistance to the aminoglycosides, kanamycin and streptomycin. Frequently observed MDR/AMR -Vibrio spp. in seafood is a potential health concern associated with seafood consumption. Our surveillance study provided an indication of the antibiotics that challenged the marine bacteria, sourced to Canadian estuaries, during and/or prior to the study period.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".