The burden and contemporary epidemiology of sepsis in children
Bibliographic record
Abstract
Sepsis is a dysregulated host response to infection that leads to life-threatening organ dysfunction. Half of the 50 million people affected by sepsis globally every year are neonates and children younger than 19 years. This burden on the paediatric population translates into a disproportionate impact on global child health in terms of years of life lost, morbidity, and lost opportunities for children to reach their developmental potential. This Series on paediatric sepsis presents the current state of diagnosis and treatment of sepsis in children, and maps the challenges in alleviating the burden on children, their families, and society. Drawing on diverse experience and multidisciplinary expertise, we offer a roadmap to improving outcomes for children with sepsis. This first paper of the Series is a narrative review of the burden of paediatric sepsis from low-income to high-income settings. Advances towards improved operationalisation of paediatric sepsis across all age groups have facilitated more standardised assessment of the Global Burden of Disease estimates of the impact of sepsis on child health, and these estimates are expected to gain further precision with the roll out of the new Phoenix criteria for sepsis. Sepsis remains one of the leading causes of childhood morbidity and mortality, with immense direct and indirect societal costs. Although substantial regional differences persist in relation to incidence, microbiological epidemiology, and outcomes, these cannot be explained by differences in income level alone. Recent insights into post-discharge sequelae after paediatric sepsis, ranging from late mortality and persistent neurodevelopmental impairment to reduced health-related quality of life, show how common post-sepsis syndrome is in children. Targeting sepsis as a key contributor to poor health outcomes in children is therefore an essential component of efforts to meet the Sustainable Development Goals.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".