Sepsis in Canadian children: a national analysis using administrative data
Bibliographic record
Abstract
BACKGROUND: Severe infection resulting in sepsis is recognized as a leading cause of morbidity and mortality worldwide. The purpose of this study is to use longitudinal, population-based data to report national-level hospital metrics, providing a current assessment of the status of sepsis hospitalizations in Canadian children. METHODS: We performed an analysis of previously abstracted data from the Canadian Institute for Health Information (CIHI) Discharge Abstract Database (DAD). Children aged 0-17 years at the time of hospital admission were identified from a cohort of patients with sepsis or severe sepsis using the International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10-CA) and the Canadian Classification of Health Interventions (CCI). Descriptive population-based statistics are reported. RESULTS: Hospitalization data for 20,130 children admitted over 5 years were reviewed. The majority of children were young, with neonates (56.3%) and infants under 2 months (18.8%) representing the majority of cases. A decline in age-adjusted hospitalization rates was demonstrated in both overall and non-severe sepsis across the study period; however, no change was demonstrated for severe sepsis. While overall in-hospital crude mortality rates did not change significantly across the study period (range 5.1%-5.4%), a significant decrease was found in children aged 3-23 months and adolescents. Multi-organ failure was reported in more than one-quarter of children with severe sepsis. Odds of mortality increased significantly with number of organs failed. CONCLUSION: Sepsis remains an important cause of morbidity and mortality in Canadian children, posing a significant burden on health care resources. Age continues to be associated with the incidence and severity of illness. Overall hospitalization rates have declined over time, as has mortality in severe sepsis. This report provides baseline metrics for future outcome-based research in Canada targeting prevention strategies and early diagnosis, as well as therapies preventing and managing organ failure.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".