Long-term outcomes after paediatric sepsis: A narrative review
Bibliographic record
Abstract
OBJECTIVES: Sepsis is one of the leading causes of mortality in the paediatric population. However, knowledge is limited around morbidity in childhood sepsis survivors. The aim of this review is to identify and critically appraise the evidence for long-term outcomes in paediatric survivors of sepsis using the Post Intensive Care Syndrome - paediatrics (PICS-p) framework. METHODS: A search for studies was undertaken in the electronic databases PubMed and Cumulative Index to Nursing and Allied Health Literature from 2010. Criteria for inclusion are as follows: participants >28 d adjusted age, diagnosed with sepsis, and follow-up after hospital discharge using a validated outcome measure. The PICS-p subdomains (cognitive, physical, emotional, and social) guided thematic synthesis of current literature. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS: In total, nine studies, involving 2136 children, using 13 different outcome measures were included in the review. Mean follow-up time was 28 d after hospital discharge with a range of 7 d to 12 m across the PICS-p domains. Physical functioning was the most examined domain explored in six studies that used four outcome measures. Morbidity in physical, cognitive, and emotional domains was still evident at 9-12 m. No literature identified explored social health. CONCLUSION(S): Overall, we identified a wide range of measures, administered at various time points in studies of sepsis survivorship in childhood. Variation in follow-up timepoints, validated tools, and restricted outcome measures highlighted the lack in understanding of this priority area. Furthermore, long-term outcome research and a cohesive understanding across all the PICS-p domains are needed to better understand this population. REGISTRATION: Not registered.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".