An Epidemiologic Survey of Pediatric Sepsis in Regional Hospitals in China*
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence, treatment, and outcomes of sepsis at regional hospitals in Huai'an, Jiangsu, China. DESIGN: Prospective data registry using a descriptive clinical epidemiologic approach through a collaborative network. SETTING: Pediatric departments in 11 regional city and county referral hospitals serving 843,000 children (exclusive of neonates). SUBJECTS: All admissions (n = 27,836) of patients from 28 days to 15 years old from September 1, 2010, to August 31, 2011. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 1,530 patients met the 2005 international consensus definition of sepsis, corresponding to an estimated incidence of 181/100,000 children, with 80% under 5 years old, and in 10% (153), severe sepsis or septic shock developed. The overall case fatality rate for sepsis was 3.5% (53/1,530) or 34.6% (53/153) in those in whom severe sepsis or septic shock developed. Treatment varied widely and in many instances did not conform to international guidelines as reflected by inadequate use of antibiotics, corticosteroids, vasoactive agents, and inotropes. CONCLUSIONS: We first report the prevalence and outcome of pediatric sepsis based on a regional hospital network in China. The diverse treatment approaches and practice at low-level clinics suggest the need for clinical implementation of internationally recognized strategy to improve the care standard in resource-limited regional hospitals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".