The Epidemiology of Childhood <i>Salmonella</i> Infections in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: The objectives were to describe the incidence, demographics, laboratory findings, and suspected sources of childhood Salmonella infections in Alberta, Canada, with a focus on preventable cases. METHODS: Data from Notifiable Disease Reports for children with nontyphoidal salmonellosis (NTS) or typhoid/paratyphoid fever from 2007 through 2015 were analyzed. RESULTS: NTS was detected from 2285 children. Bacteremia was documented in 55 cases (2.4%), whereas a single infant had NTS meningitis. The suspected source was food (N = 577; 25.3%) followed by animal or animal manure contact (N = 426; 18.6%), of which a reptile was the suspected source in 264 cases (11.5%). There were 44 outbreaks with none sharing the same food source. Ninety-five children were diagnosed with typhoid/paratyphoid fever, of which 48 cases (51%) were typhoid cases in unimmunized children 2 years or older. CONCLUSIONS: There are still ∼275 pediatric cases of Salmonella infection in Alberta annually, the bulk of which are preventable. APPLICATION: Public education about reptile exposure, food safety, and pretravel immunizations could potentially prevent many cases of Salmonella infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".