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Record W2324477456 · doi:10.1097/pcc.0000000000000247

An Epidemiologic Survey of Pediatric Sepsis in Regional Hospitals in China*

2014· article· en· W2324477456 on OpenAlexaff
Yuanyuan Wang, Bo Sun, Hongni Yue, Xiaofei Lin, Bing Li, Xiaochun Yang, Chunming Shan, Yujin Fan, Maotian Dong, Yixing Zhang, Wenlong Lin, Xiaofeng Zuo, Ping Su, Jinzhong Xu, Niranjan Kissoon

Bibliographic record

VenuePediatric Critical Care Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineSepsisCase fatality rateReferralSeptic shockIncidence (geometry)Emergency medicinePsychological interventionIntensive care medicinePediatricsEpidemiologyFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.412
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations73
Published2014
Admission routes1
Has abstractyes

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