Current pathogen profile for bacteremia in a tertiary care pediatric emergency department in Canada
Bibliographic record
Abstract
Abstract Importance Bacteremia in children can lead to septic shock, meningitis or death. Knowledge of common pathogens and predictors is crucial for appropriate lifesaving management. Objective We aimed to identify pathogens and associated variables in children with bacteremia presenting to the emergency department (ED). Methods This retrospective cohort study was conducted in a tertiary pediatric hospital in Montreal, Canada (2018-2024). The full cohort included all children with a positive blood culture drawn in the ED while this study focused on cases of true bacteremia identified through medical record evaluation by two raters. The primary outcome was pathogen distribution. Potential pathogen-associated independent variables included demographics, vaccination status, recent travel, clinical presentation, and known risk factors. The primary analysis focused on pathogen prevalence, while secondary analysis assessed associations between predictors and pathogens using Chi-squared tests. Results Among 368 bacteremia cases (median age: 39 months), the most common pathogens were Staphylococcus aureus (25%), Escherichia coli (16%), Streptococcus pneumoniae (9.0%) and non-typhoidal Salmonella (8.7%). Overall, 175 (48%) had risk factors, including internal devices (n = 80, 22%), age less than 3 months (n = 59, 16%), or immunosuppression (n = 55, 15%). Significant associations emerged: 34% (n = 38) of Staphylococcus spp. infections involved internal devices, 62% (n = 24) of Salmonella spp. infections had recent travel, and 38% (n = 22) of E. coli infections and 69% (n = 11) of group B Streptococcus infections occurred in infants aged under 3 months. Conclusions Our study highlights current key pathogens and associated predictors in pediatric bacteremia. Travel history, internal hardware, age, and immunosuppression are crucial for clinicians to consider in its assessment and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".