A Descriptive Study of Human <i>Salmonella</i> Serotype Typhimurium Infections Reported in Ontario from 1990 to 1997
Bibliographic record
Abstract
BACKGROUND: Salmonella infections cause gastrointestinal and systemic diseases worldwide and are the leading causes of food-borne illnesses in North America (1-4). Salmonella serotype typhimurium (ST), in particular, is increasingly becoming a major public health concern because of its ability to acquire multiple resistant genes (5,6). OBJECTIVE: To describe demographic, temporal and geographical distributions, and reported risk factors of nonoutbreak cases of ST reported to a surveillance system in Ontario. METHODOLOGY: Descriptive analyses were performed on data on salmonellosis cases reported in Ontario between 1990 and 1998. Direct age- and sex-standardized rates were computed, and temporal trend analyses were performed using simple linear regression and a general additive model with a locally weighted regression (LOESS) smoother. RESULTS: The mean annual rates of infections with all Salmonella serotypes and with ST were 27 cases per 100,000 persons and 3.7 cases per 100,000 persons, respectively. Males and children under five years of age had significantly higher rates of both ST and ST definitive type 104 (DT104) infections. There was also evidence of temporal clustering of all strains of Salmonella, with significantly more cases being reported during the summer. Significantly higher rates of ST DT104 were observed in urban areas compared with rural areas, suggesting potential differences in the geographical distribution of risk factors. CONCLUSIONS: Information on demographic, temporal and geographical distributions, and risk factors is critical in planning disease control strategies. Further prospective analytical observation studies are needed to gain a better understanding of the epidemiology of ST and ST DT104 in Ontario, which will better guide disease control decisions.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".