Increased Burden of Illness Associated with Antimicrobial‐Resistant<i>Salmonella enterica</i>Serotype Typhimurium Infections
Bibliographic record
Abstract
This study investigated the burden of illness associated with 440 cases of Salmonella enterica serotype Typhimurium infection identified in Canada between December 1999 and November 2000. We categorized case subjects' infections by definitive phage type 104 (DT104) and antimicrobial-resistance patterns. These variables were then investigated as risk factors for hospitalization. Hospitalization was more likely to occur among case subjects whose infections were resistant to at least ampicillin, chloramphenicol and/or kanamycin, streptomycin, sulphamethoxazole, and tetracycline (R-type AK/CSSuT; odds ratio [OR], 2.3; P=.003), compared with case subjects with AK/CSSuT-susceptible infections, and among case subjects with non-DT104 R-type AKSSuT infections (OR, 3.6; P=.005), compared with case subjects with non-DT104 AKSSuT-susceptible infections. In contrast, hospitalization rates did not differ between case subjects with DT104 infections and case subjects with non-DT104 infections or between case subjects with DT104 R-type ACSSuT infections and case subjects with DT104 ACSSuT-susceptible infections. We estimated that 57% of the hospitalizations among AK/CSSuT case subjects and 72% of the hospitalizations among non-DT104 AKSSuT case subjects were attributable to the resistance patterns of the 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 | 0.000 |
| 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".