Susceptibility to childhood sepsis, contemporary management, and future directions
Bibliographic record
Abstract
Sepsis disproportionally affects children across all health-care settings and is one of the leading causes of morbidity and mortality in neonatal and paediatric age groups. As shown in the first paper in this Series, the age-specific incidence of sepsis is highest during the first years of life, before approaching adult incidence rates during adolescence. In the second paper in this Series, we focus on the unique susceptibility of paediatric patients to sepsis and how the underlying dysregulated host response relates to developmental aspects of children's immune system, genetic, perinatal, and environmental factors, and comorbidities and socioeconomic determinants of health, which often differ between children and adults. State-of-the-art clinical management of paediatric sepsis is organised around three treatment pillars-diagnosis, early resuscitation, and titration of advanced care-and we examine available treatment guidelines and the limitations of their supporting evidence. Serious evidence gaps remain in key areas of paediatric sepsis care, especially surrounding recognition, common interventions, and survivor support, and to this end we offer a research roadmap for the next decade that could accelerate targeted diagnostics and personalised use of immunomodulation. However, improving outcomes for children with sepsis relies fundamentally on systematic quality improvement in both recognition and treatment, which is the theme of the third paper in this Series. Digital health, as shown in the fourth and final paper of this Series, holds promising potential in breaking down the barriers that hinder progress in paediatric sepsis care and, ultimately, global child health.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".