Antimicrobial Resistance among <i>Salmonella</i> and <i>Shigella</i> Isolates in Five Canadian Provinces (1997 to 2000)
Bibliographic record
Abstract
OBJECTIVE: To describe rates of antimicrobial resistance (AMR) among Salmonella and Shigella isolates reported in five Canadian provinces, focusing on clinically important antimicrobials. METHODS: The authors retrospectively investigated AMR rates among 6219 Salmonella and 1673 Shigella isolates submitted to provincial public health laboratories in Alberta, Newfoundland and Labrador, Ontario, Prince Edward Island and Saskatchewan from 1997 to 2000; these isolates were estimated to represent 41% of Salmonella cases and 72% of Shigella cases reported by the study provinces. RESULTS: Among Salmonella isolates, 27% (1704 of 6215) were resistant to ampicillin, 2.2% (135 of 6122) to trimethoprim/ sulfamethoxazole, 1.5% (14 of 938) to nalidixic acid, 1.2% (one of 84) to lomafloxacin and 0.08% (five of 6163) to ciprofloxacin. Among Shigella isolates, 70% (1144 of 1643) were resistant to trimethoprim/sulfamethoxazole, 65% (1079 of 1672) to ampicillin, 3.1% (eight of 262) to nalidixic acid, 0.49% (eight of 1636) to ciprofloxacin, 0.14% (one of 700) to ceftriaxone and 0.08% (one of 1292) to ceftazidime. CONCLUSIONS: Higher rates of resistance to clinically important antimicrobials (including ciprofloxacin) were observed among both Salmonella and Shigella isolates than has previously been reported. Current Canadian data on rates of AMR for these pathogens are required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".