Systems for Pediatric Sepsis: A Global Survey
Bibliographic record
Abstract
OBJECTIVES: To evaluate the resources available for early diagnosis and treatment of paediatric sepsis at hospitals in developing and developed countries. METHODS: This was a voluntary online survey involving 101 hospitals from 41 countries solicited through the World Federation of Pediatric Intensive and Critical Care Societies contact list and website. The survey was designed to assess the spectrum of sepsis epidemiology, patterns of applied therapies, availability of resources and barriers to optimal sepsis treatment. RESULTS: Ninety per cent of respondents represented a tertiary or general hospital with paediatric intensive care facilities, including 63% from developed countries. Adequate triage services were absent in more than 20% of centres. Insufficiently trained personnel and lack of a sepsis protocol was reported in 40% of all sites. While there were specific guidelines for sepsis management in 78% of centres (n = 100), protocols for assessing sepsis patients were not applied in nearly 70% of centres. Lack of parental recognition of sepsis and failure of referring centres to diagnose sepsis were identified as major barriers by more than 50% of respondents. CONCLUSIONS: Even among centres with no significant resource constraints and advanced medical systems, significant deficits in sepsis care exist. Early recognition and management remain a key issue and may be addressed through improved triage, augmented support for referring centres and public awareness. Focussed research is necessary at the institutional level to identify and address specific barriers.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".